datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
emozilla/hermes2-tokenized-llama-alpaca | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int32
splits:
- name: train
num_bytes: 18472606644
num_examples: 1001551
download_size: 1213968493
dataset_size: 18472606644
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_cloudyu__Mixtral_7Bx2_MoE | ---
pretty_name: Evaluation run of cloudyu/Mixtral_7Bx2_MoE
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cloudyu/Mixtral_7Bx2_MoE](https://huggingface.co/cloudyu/Mixtral_7Bx2_MoE) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cloudyu__Mixtral_7Bx2_MoE\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-30T02:50:07.869164](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Mixtral_7Bx2_MoE/blob/main/results_2023-12-30T02-50-07.869164.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\": 0.6544585464368745,\n\
\ \"acc_stderr\": 0.03200088458372204,\n \"acc_norm\": 0.6547015253813414,\n\
\ \"acc_norm_stderr\": 0.032655083043548944,\n \"mc1\": 0.5348837209302325,\n\
\ \"mc1_stderr\": 0.017460849975873972,\n \"mc2\": 0.6723305286969269,\n\
\ \"mc2_stderr\": 0.01523375567555562\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6834470989761092,\n \"acc_stderr\": 0.013592431519068079,\n\
\ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266129\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7013543118900617,\n\
\ \"acc_stderr\": 0.004567287775700558,\n \"acc_norm\": 0.8745269866560446,\n\
\ \"acc_norm_stderr\": 0.003305774980082251\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720386\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\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_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-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396262,\n\
\ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396262\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146267,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146267\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4365079365079365,\n \"acc_stderr\": 0.025542846817400496,\n \"\
acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.025542846817400496\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5158730158730159,\n\
\ \"acc_stderr\": 0.044698818540726076,\n \"acc_norm\": 0.5158730158730159,\n\
\ \"acc_norm_stderr\": 0.044698818540726076\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \
\ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6848739495798319,\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\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8532110091743119,\n \"acc_stderr\": 0.01517314184512625,\n \"\
acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.01517314184512625\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.034063153607115086,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.034063153607115086\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\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\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\
\ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\
\ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\
\ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4558659217877095,\n\
\ \"acc_stderr\": 0.01665722942458631,\n \"acc_norm\": 0.4558659217877095,\n\
\ \"acc_norm_stderr\": 0.01665722942458631\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.02526169121972948,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.02526169121972948\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.02575586592263295,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.02575586592263295\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.02447722285613511,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.02447722285613511\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873862,\n \
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873862\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46740547588005216,\n\
\ \"acc_stderr\": 0.012743072942653354,\n \"acc_norm\": 0.46740547588005216,\n\
\ \"acc_norm_stderr\": 0.012743072942653354\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031204,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031204\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\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\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5348837209302325,\n\
\ \"mc1_stderr\": 0.017460849975873972,\n \"mc2\": 0.6723305286969269,\n\
\ \"mc2_stderr\": 0.01523375567555562\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8121546961325967,\n \"acc_stderr\": 0.010977481103435091\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6846095526914329,\n \
\ \"acc_stderr\": 0.012799353675801832\n }\n}\n```"
repo_url: https://huggingface.co/cloudyu/Mixtral_7Bx2_MoE
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|arc:challenge|25_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|gsm8k|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hellaswag|10_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-50-07.869164.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-30T02-50-07.869164.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- '**/details_harness|winogrande|5_2023-12-30T02-50-07.869164.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-30T02-50-07.869164.parquet'
- config_name: results
data_files:
- split: 2023_12_30T02_50_07.869164
path:
- results_2023-12-30T02-50-07.869164.parquet
- split: latest
path:
- results_2023-12-30T02-50-07.869164.parquet
---
# Dataset Card for Evaluation run of cloudyu/Mixtral_7Bx2_MoE
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cloudyu/Mixtral_7Bx2_MoE](https://huggingface.co/cloudyu/Mixtral_7Bx2_MoE) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_cloudyu__Mixtral_7Bx2_MoE",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-30T02:50:07.869164](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Mixtral_7Bx2_MoE/blob/main/results_2023-12-30T02-50-07.869164.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": 0.6544585464368745,
"acc_stderr": 0.03200088458372204,
"acc_norm": 0.6547015253813414,
"acc_norm_stderr": 0.032655083043548944,
"mc1": 0.5348837209302325,
"mc1_stderr": 0.017460849975873972,
"mc2": 0.6723305286969269,
"mc2_stderr": 0.01523375567555562
},
"harness|arc:challenge|25": {
"acc": 0.6834470989761092,
"acc_stderr": 0.013592431519068079,
"acc_norm": 0.712457337883959,
"acc_norm_stderr": 0.013226719056266129
},
"harness|hellaswag|10": {
"acc": 0.7013543118900617,
"acc_stderr": 0.004567287775700558,
"acc_norm": 0.8745269866560446,
"acc_norm_stderr": 0.003305774980082251
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720386,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720386
},
"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": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.0358687928008034,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.0358687928008034
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.45098039215686275,
"acc_stderr": 0.04951218252396262,
"acc_norm": 0.45098039215686275,
"acc_norm_stderr": 0.04951218252396262
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146267,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146267
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555498,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.025542846817400496,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.025542846817400496
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5158730158730159,
"acc_stderr": 0.044698818540726076,
"acc_norm": 0.5158730158730159,
"acc_norm_stderr": 0.044698818540726076
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
"acc_stderr": 0.023415293433568525,
"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.023415293433568525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.03192271569548301,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.03192271569548301
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8131313131313131,
"acc_stderr": 0.027772533334218967,
"acc_norm": 0.8131313131313131,
"acc_norm_stderr": 0.027772533334218967
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603348,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603348
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6794871794871795,
"acc_stderr": 0.02366129639396428,
"acc_norm": 0.6794871794871795,
"acc_norm_stderr": 0.02366129639396428
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028593,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028593
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6848739495798319,
"acc_stderr": 0.030176808288974337,
"acc_norm": 0.6848739495798319,
"acc_norm_stderr": 0.030176808288974337
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8532110091743119,
"acc_stderr": 0.01517314184512625,
"acc_norm": 0.8532110091743119,
"acc_norm_stderr": 0.01517314184512625
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.034063153607115086,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.034063153607115086
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02552472232455334,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455334
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
"acc_stderr": 0.026160568246601446,
"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601446
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990947,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990947
},
"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": 0.7791411042944786,
"acc_stderr": 0.03259177392742178,
"acc_norm": 0.7791411042944786,
"acc_norm_stderr": 0.03259177392742178
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.020588491316092375,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092375
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8339719029374202,
"acc_stderr": 0.013306478243066302,
"acc_norm": 0.8339719029374202,
"acc_norm_stderr": 0.013306478243066302
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7052023121387283,
"acc_stderr": 0.024547617794803828,
"acc_norm": 0.7052023121387283,
"acc_norm_stderr": 0.024547617794803828
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4558659217877095,
"acc_stderr": 0.01665722942458631,
"acc_norm": 0.4558659217877095,
"acc_norm_stderr": 0.01665722942458631
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.02526169121972948,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.02526169121972948
},
"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": 0.7376543209876543,
"acc_stderr": 0.02447722285613511,
"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.02447722285613511
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.46808510638297873,
"acc_stderr": 0.029766675075873862,
"acc_norm": 0.46808510638297873,
"acc_norm_stderr": 0.029766675075873862
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46740547588005216,
"acc_stderr": 0.012743072942653354,
"acc_norm": 0.46740547588005216,
"acc_norm_stderr": 0.012743072942653354
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6801470588235294,
"acc_stderr": 0.028332959514031204,
"acc_norm": 0.6801470588235294,
"acc_norm_stderr": 0.028332959514031204
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6781045751633987,
"acc_stderr": 0.018901015322093092,
"acc_norm": 0.6781045751633987,
"acc_norm_stderr": 0.018901015322093092
},
"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": 0.7224489795918367,
"acc_stderr": 0.028666857790274648,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274648
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8557213930348259,
"acc_stderr": 0.024845753212306053,
"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.024845753212306053
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8421052631578947,
"acc_stderr": 0.027966785859160893,
"acc_norm": 0.8421052631578947,
"acc_norm_stderr": 0.027966785859160893
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5348837209302325,
"mc1_stderr": 0.017460849975873972,
"mc2": 0.6723305286969269,
"mc2_stderr": 0.01523375567555562
},
"harness|winogrande|5": {
"acc": 0.8121546961325967,
"acc_stderr": 0.010977481103435091
},
"harness|gsm8k|5": {
"acc": 0.6846095526914329,
"acc_stderr": 0.012799353675801832
}
}
```
## 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] |
tasksource/mtop | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
tags:
- multilingual
- intent
multilinguality:
- multilingual
---
https://arxiv.org/pdf/2008.09335.pdf
```
@article{li2020mtop,
title={MTOP: A comprehensive multilingual task-oriented semantic parsing benchmark},
author={Li, Haoran and Arora, Abhinav and Chen, Shuohui and Gupta, Anchit and Gupta, Sonal and Mehdad, Yashar},
journal={arXiv preprint arXiv:2008.09335},
year={2020}
}
``` |
totally-not-an-llm/sharegpt-hyperfiltered-3k | ---
license: apache-2.0
---
# sharegpt-hyperfiltered-3k
90k sharegpt convos brought down to ~3k (3243) via language filtering, keyword detection, deduping, and regex. Following things were done:
- Deduplication on first message from human
- Remove non-English convos
- Remove censorship, refusals, and alignment
- Remove incorrect/low-quality answers
- Remove creative tasks
- ChatGPT's creative outputs are very censored and robotic; I think the base model can do better.
- Remove URLs
- Remove cutoffs
- Remove math/reasoning questions
- It sucks without CoT prompting, so this data should be mixed with better reasoning examples like OpenOrca or Dolphin.
|
Nicolas-BZRD/BALO_opendata | ---
language:
- fr
license: odc-by
size_categories:
- 100K<n<1M
pretty_name: Bulletin of mandatory legal notices
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1106418284
num_examples: 135575
download_size: 439587100
dataset_size: 1106418284
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- finance
- legal
---
# BALO (Bulletin of mandatory legal notices)
Announcements published in the [BALO](https://www.data.gouv.fr/en/datasets/balo/) (Bulletin des annonces lรฉgales obligatoires).
The BALO publishes compulsory notices for companies making public offerings and for banking and credit institutions. The announcements relate to all financial transactions, accounting documents and notices of shareholders' general meetings. |
open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-XLCTX-v3 | ---
pretty_name: Evaluation run of adamo1139/Yi-34B-200K-AEZAKMI-XLCTX-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [adamo1139/Yi-34B-200K-AEZAKMI-XLCTX-v3](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-XLCTX-v3)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-XLCTX-v3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-25T20:39:59.755958](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-XLCTX-v3/blob/main/results_2024-03-25T20-39-59.755958.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\": 0.7371837196536577,\n\
\ \"acc_stderr\": 0.029082616406592553,\n \"acc_norm\": 0.7449178946334059,\n\
\ \"acc_norm_stderr\": 0.02962575814350032,\n \"mc1\": 0.27539779681762544,\n\
\ \"mc1_stderr\": 0.015638135667775516,\n \"mc2\": 0.37141750146639024,\n\
\ \"mc2_stderr\": 0.0145273666098832\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6186006825938567,\n \"acc_stderr\": 0.014194389086685242,\n\
\ \"acc_norm\": 0.6484641638225256,\n \"acc_norm_stderr\": 0.013952413699600931\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6444931288587931,\n\
\ \"acc_stderr\": 0.004776883632722613,\n \"acc_norm\": 0.8476399123680541,\n\
\ \"acc_norm_stderr\": 0.0035863512488636074\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.725925925925926,\n\
\ \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.725925925925926,\n\
\ \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549915,\n\
\ \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549915\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\
\ \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \
\ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372274,\n\
\ \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372274\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8541666666666666,\n\
\ \"acc_stderr\": 0.029514245964291762,\n \"acc_norm\": 0.8541666666666666,\n\
\ \"acc_norm_stderr\": 0.029514245964291762\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\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\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7283236994219653,\n\
\ \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.7283236994219653,\n\
\ \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.04897104952726366,\n\
\ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.04897104952726366\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7361702127659574,\n \"acc_stderr\": 0.028809989854102956,\n\
\ \"acc_norm\": 0.7361702127659574,\n \"acc_norm_stderr\": 0.028809989854102956\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5789473684210527,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7655172413793103,\n \"acc_stderr\": 0.035306258743465914,\n\
\ \"acc_norm\": 0.7655172413793103,\n \"acc_norm_stderr\": 0.035306258743465914\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.6587301587301587,\n \"acc_stderr\": 0.02441923496681907,\n \"\
acc_norm\": 0.6587301587301587,\n \"acc_norm_stderr\": 0.02441923496681907\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\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8741935483870967,\n\
\ \"acc_stderr\": 0.018865834288030008,\n \"acc_norm\": 0.8741935483870967,\n\
\ \"acc_norm_stderr\": 0.018865834288030008\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.625615763546798,\n \"acc_stderr\": 0.03405155380561952,\n\
\ \"acc_norm\": 0.625615763546798,\n \"acc_norm_stderr\": 0.03405155380561952\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\"\
: 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8424242424242424,\n \"acc_stderr\": 0.028450388805284357,\n\
\ \"acc_norm\": 0.8424242424242424,\n \"acc_norm_stderr\": 0.028450388805284357\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\
acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295141,\n\
\ \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295141\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7974358974358975,\n \"acc_stderr\": 0.020377660970371397,\n\
\ \"acc_norm\": 0.7974358974358975,\n \"acc_norm_stderr\": 0.020377660970371397\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.45555555555555555,\n \"acc_stderr\": 0.03036486250482443,\n \
\ \"acc_norm\": 0.45555555555555555,\n \"acc_norm_stderr\": 0.03036486250482443\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\
\ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.48344370860927155,\n \"acc_stderr\": 0.0408024418562897,\n \"\
acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.0408024418562897\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9045871559633027,\n \"acc_stderr\": 0.012595899282335801,\n \"\
acc_norm\": 0.9045871559633027,\n \"acc_norm_stderr\": 0.012595899282335801\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6851851851851852,\n \"acc_stderr\": 0.031674687068289784,\n \"\
acc_norm\": 0.6851851851851852,\n \"acc_norm_stderr\": 0.031674687068289784\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8872549019607843,\n \"acc_stderr\": 0.02219857103945679,\n \"\
acc_norm\": 0.8872549019607843,\n \"acc_norm_stderr\": 0.02219857103945679\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8860759493670886,\n \"acc_stderr\": 0.020681745135884565,\n \
\ \"acc_norm\": 0.8860759493670886,\n \"acc_norm_stderr\": 0.020681745135884565\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7713004484304933,\n\
\ \"acc_stderr\": 0.0281882400469292,\n \"acc_norm\": 0.7713004484304933,\n\
\ \"acc_norm_stderr\": 0.0281882400469292\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8320610687022901,\n \"acc_stderr\": 0.032785485373431386,\n\
\ \"acc_norm\": 0.8320610687022901,\n \"acc_norm_stderr\": 0.032785485373431386\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.859504132231405,\n \"acc_stderr\": 0.03172233426002159,\n \"acc_norm\"\
: 0.859504132231405,\n \"acc_norm_stderr\": 0.03172233426002159\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8703703703703703,\n\
\ \"acc_stderr\": 0.03247224389917947,\n \"acc_norm\": 0.8703703703703703,\n\
\ \"acc_norm_stderr\": 0.03247224389917947\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.026321383198783674,\n\
\ \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.026321383198783674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.034926064766237906,\n\
\ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.034926064766237906\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9230769230769231,\n\
\ \"acc_stderr\": 0.017456987872436186,\n \"acc_norm\": 0.9230769230769231,\n\
\ \"acc_norm_stderr\": 0.017456987872436186\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8952745849297573,\n\
\ \"acc_stderr\": 0.010949664098633365,\n \"acc_norm\": 0.8952745849297573,\n\
\ \"acc_norm_stderr\": 0.010949664098633365\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8179190751445087,\n \"acc_stderr\": 0.020776761102512982,\n\
\ \"acc_norm\": 0.8179190751445087,\n \"acc_norm_stderr\": 0.020776761102512982\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5776536312849162,\n\
\ \"acc_stderr\": 0.016519594275297114,\n \"acc_norm\": 0.5776536312849162,\n\
\ \"acc_norm_stderr\": 0.016519594275297114\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8300653594771242,\n \"acc_stderr\": 0.021505383121231368,\n\
\ \"acc_norm\": 0.8300653594771242,\n \"acc_norm_stderr\": 0.021505383121231368\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8135048231511254,\n\
\ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.8135048231511254,\n\
\ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.020581466138257138,\n\
\ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.020581466138257138\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5886524822695035,\n \"acc_stderr\": 0.029354911159940975,\n \
\ \"acc_norm\": 0.5886524822695035,\n \"acc_norm_stderr\": 0.029354911159940975\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5827900912646675,\n\
\ \"acc_stderr\": 0.012593959992906427,\n \"acc_norm\": 0.5827900912646675,\n\
\ \"acc_norm_stderr\": 0.012593959992906427\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7977941176470589,\n \"acc_stderr\": 0.024398192986654924,\n\
\ \"acc_norm\": 0.7977941176470589,\n \"acc_norm_stderr\": 0.024398192986654924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.795751633986928,\n \"acc_stderr\": 0.016309755848361536,\n \
\ \"acc_norm\": 0.795751633986928,\n \"acc_norm_stderr\": 0.016309755848361536\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8040816326530612,\n \"acc_stderr\": 0.025409301953225678,\n\
\ \"acc_norm\": 0.8040816326530612,\n \"acc_norm_stderr\": 0.025409301953225678\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\
\ \"acc_stderr\": 0.022076326101824636,\n \"acc_norm\": 0.8905472636815921,\n\
\ \"acc_norm_stderr\": 0.022076326101824636\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\
\ \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n\
\ \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.02410338420207286,\n\
\ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02410338420207286\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.27539779681762544,\n\
\ \"mc1_stderr\": 0.015638135667775516,\n \"mc2\": 0.37141750146639024,\n\
\ \"mc2_stderr\": 0.0145273666098832\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8105761641673244,\n \"acc_stderr\": 0.011012790432989247\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4404852160727824,\n \
\ \"acc_stderr\": 0.013674572131693884\n }\n}\n```"
repo_url: https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-XLCTX-v3
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_25T20_39_59.755958
path:
- '**/details_harness|arc:challenge|25_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|gsm8k|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hellaswag|10_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-25T20-39-59.755958.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-25T20-39-59.755958.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- '**/details_harness|winogrande|5_2024-03-25T20-39-59.755958.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-25T20-39-59.755958.parquet'
- config_name: results
data_files:
- split: 2024_03_25T20_39_59.755958
path:
- results_2024-03-25T20-39-59.755958.parquet
- split: latest
path:
- results_2024-03-25T20-39-59.755958.parquet
---
# Dataset Card for Evaluation run of adamo1139/Yi-34B-200K-AEZAKMI-XLCTX-v3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [adamo1139/Yi-34B-200K-AEZAKMI-XLCTX-v3](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-XLCTX-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-XLCTX-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-25T20:39:59.755958](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-XLCTX-v3/blob/main/results_2024-03-25T20-39-59.755958.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": 0.7371837196536577,
"acc_stderr": 0.029082616406592553,
"acc_norm": 0.7449178946334059,
"acc_norm_stderr": 0.02962575814350032,
"mc1": 0.27539779681762544,
"mc1_stderr": 0.015638135667775516,
"mc2": 0.37141750146639024,
"mc2_stderr": 0.0145273666098832
},
"harness|arc:challenge|25": {
"acc": 0.6186006825938567,
"acc_stderr": 0.014194389086685242,
"acc_norm": 0.6484641638225256,
"acc_norm_stderr": 0.013952413699600931
},
"harness|hellaswag|10": {
"acc": 0.6444931288587931,
"acc_stderr": 0.004776883632722613,
"acc_norm": 0.8476399123680541,
"acc_norm_stderr": 0.0035863512488636074
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-anatomy|5": {
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"acc_stderr": 0.03853254836552003,
"acc_norm": 0.725925925925926,
"acc_norm_stderr": 0.03853254836552003
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.868421052631579,
"acc_norm_stderr": 0.027508689533549915
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_stderr": 0.024262979839372274,
"acc_norm": 0.8075471698113208,
"acc_norm_stderr": 0.024262979839372274
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.8541666666666666,
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},
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"acc_norm": 0.48,
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm": 0.7283236994219653,
"acc_norm_stderr": 0.03391750322321659
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5882352941176471,
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"acc_norm": 0.5882352941176471,
"acc_norm_stderr": 0.04897104952726366
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7361702127659574,
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"acc_norm_stderr": 0.028809989854102956
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5789473684210527,
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"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.046446020912223177
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7655172413793103,
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"acc_norm": 0.7655172413793103,
"acc_norm_stderr": 0.035306258743465914
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.6587301587301587,
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"acc_norm": 0.6587301587301587,
"acc_norm_stderr": 0.02441923496681907
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
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"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.8741935483870967,
"acc_norm_stderr": 0.018865834288030008
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm_stderr": 0.03405155380561952
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.78,
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9637305699481865,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7974358974358975,
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"acc_norm": 0.7974358974358975,
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},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.45555555555555555,
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"acc_norm_stderr": 0.03036486250482443
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8445378151260504,
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"acc_norm_stderr": 0.023536818625398904
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.48344370860927155,
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"acc_norm": 0.48344370860927155,
"acc_norm_stderr": 0.0408024418562897
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9045871559633027,
"acc_stderr": 0.012595899282335801,
"acc_norm": 0.9045871559633027,
"acc_norm_stderr": 0.012595899282335801
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6851851851851852,
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"acc_norm": 0.6851851851851852,
"acc_norm_stderr": 0.031674687068289784
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8872549019607843,
"acc_stderr": 0.02219857103945679,
"acc_norm": 0.8872549019607843,
"acc_norm_stderr": 0.02219857103945679
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8860759493670886,
"acc_stderr": 0.020681745135884565,
"acc_norm": 0.8860759493670886,
"acc_norm_stderr": 0.020681745135884565
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7713004484304933,
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"acc_norm": 0.7713004484304933,
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},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm_stderr": 0.032785485373431386
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.859504132231405,
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},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8703703703703703,
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},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm_stderr": 0.026321383198783674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5089285714285714,
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"acc_norm": 0.5089285714285714,
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},
"harness|hendrycksTest-management|5": {
"acc": 0.8543689320388349,
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"acc_norm": 0.8543689320388349,
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},
"harness|hendrycksTest-marketing|5": {
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"acc_norm": 0.9230769230769231,
"acc_norm_stderr": 0.017456987872436186
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.87,
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},
"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.5776536312849162,
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},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8300653594771242,
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"acc_norm": 0.8300653594771242,
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},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.8135048231511254,
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.8364197530864198,
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.022076326101824636
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.92,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.5662650602409639,
"acc_stderr": 0.03858158940685515,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685515
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.02410338420207286,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.02410338420207286
},
"harness|truthfulqa:mc|0": {
"mc1": 0.27539779681762544,
"mc1_stderr": 0.015638135667775516,
"mc2": 0.37141750146639024,
"mc2_stderr": 0.0145273666098832
},
"harness|winogrande|5": {
"acc": 0.8105761641673244,
"acc_stderr": 0.011012790432989247
},
"harness|gsm8k|5": {
"acc": 0.4404852160727824,
"acc_stderr": 0.013674572131693884
}
}
```
## 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] |
AK-12/Medical_Data | ---
task_categories:
- question-answering
language:
- en
--- |
EddieChen372/python_vul_cvefix | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': CWE-22
'1': CWE-79
'2': CWE-89
'3': CWE-352
'4': CWE-601
'5': CWE-94
- name: code_before
dtype: string
- name: code_after
dtype: string
- name: label_text
dtype: string
- name: deleted
struct:
- name: code
sequence: string
- name: line_no
sequence: int64
- name: added
struct:
- name: code
sequence: string
- name: line_no
sequence: int64
- name: normalized_code_before
dtype: string
- name: normalized_code_after
dtype: string
- name: before_doc_string_pos
sequence: int64
- name: after_doc_string_pos
sequence: int64
splits:
- name: train
num_bytes: 15101828.429268293
num_examples: 204
- name: test
num_bytes: 3822268.0
num_examples: 52
download_size: 6388923
dataset_size: 18924096.429268293
---
# Dataset Card for "python_vul_cvefix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pepoo20/Symbol_Elementary_Math | ---
dataset_info:
- config_name: Greatest_Common_Divisor
features:
- name: Text Responses
dtype: string
- name: Symbolic Form
dtype: string
- name: Symbolic Answers
dtype: string
- name: WordProblem
dtype: bool
splits:
- name: train
num_bytes: 7544
num_examples: 20
download_size: 6698
dataset_size: 7544
- config_name: Inequalities
features:
- name: Text Responses
dtype: string
- name: Symbolic Form
dtype: string
- name: Symbolic Answers
dtype: string
- name: WordProblem
dtype: bool
splits:
- name: train
num_bytes: 14946
num_examples: 40
download_size: 12024
dataset_size: 14946
- config_name: Least_Common_Multiple
features:
- name: Text Responses
dtype: string
- name: Symbolic Form
dtype: string
- name: Symbolic Answers
dtype: string
- name: WordProblem
dtype: bool
splits:
- name: train
num_bytes: 9558
num_examples: 20
download_size: 8050
dataset_size: 9558
- config_name: System_Linear
features:
- name: Text Responses
dtype: string
- name: Symbolic Form
dtype: string
- name: Symbolic Answers
dtype: string
- name: WordProblem
dtype: bool
splits:
- name: train
num_bytes: 10518
num_examples: 20
download_size: 9319
dataset_size: 10518
configs:
- config_name: Greatest_Common_Divisor
data_files:
- split: train
path: Greatest_Common_Divisor/train-*
- config_name: Inequalities
data_files:
- split: train
path: Inequalities/train-*
- config_name: Least_Common_Multiple
data_files:
- split: train
path: Least_Common_Multiple/train-*
- config_name: System_Linear
data_files:
- split: train
path: System_Linear/train-*
---
|
CyberHarem/vajra_granbluefantasy | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of vajra (Granblue Fantasy)
This is the dataset of vajra (Granblue Fantasy), containing 435 images and their tags.
The core tags of this character are `animal_ears, blonde_hair, short_hair, dog_ears, bangs, braid, hair_ornament, breasts, tail, dog_tail, dog_girl, small_breasts, yellow_eyes, brown_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 | 435 | 715.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vajra_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 435 | 382.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vajra_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1110 | 852.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vajra_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 435 | 620.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vajra_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1110 | 1.23 GiB | [Download](https://huggingface.co/datasets/CyberHarem/vajra_granbluefantasy/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/vajra_granbluefantasy',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, detached_sleeves, erune, katana, looking_at_viewer, shimenawa, solo, bare_shoulders, holding_sword, wide_sleeves, sheath, smile, open_mouth, simple_background, white_background, black_pantyhose, blush, pelvic_curtain, japanese_clothes |
| 1 | 7 |  |  |  |  |  | 1girl, detached_sleeves, erune, looking_at_viewer, open_mouth, shimenawa, smile, solo, bare_shoulders, katana, pantyhose, wide_sleeves, blush, japanese_clothes, sheath |
| 2 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blush, detached_sleeves, erune, fang, looking_at_viewer, open_mouth, shimenawa, solo, wide_sleeves, :d, black_pantyhose, pelvic_curtain, white_background, claw_pose, japanese_clothes, simple_background |
| 3 | 14 |  |  |  |  |  | 1girl, detached_sleeves, erune, looking_at_viewer, shimenawa, solo, wide_sleeves, bare_shoulders, black_pantyhose, blush, open_mouth, simple_background, white_background, japanese_clothes, pelvic_curtain, :d |
| 4 | 6 |  |  |  |  |  | 1girl, bare_shoulders, detached_sleeves, erune, hair_flower, kimono, looking_at_viewer, smile, solo, blush, nail_polish, obi, red_nails, wide_sleeves, open_mouth, simple_background, white_background, claw_pose, petals, upper_body |
| 5 | 8 |  |  |  |  |  | 1girl, bare_shoulders, blush, erune, kimono, looking_at_viewer, obi, solo, wide_sleeves, detached_sleeves, hair_flower, long_sleeves, open_mouth, :d, oil-paper_umbrella |
| 6 | 7 |  |  |  |  |  | 1girl, erune, hair_flower, looking_at_viewer, open_mouth, solo, white_flower, blush, red_neckerchief, upper_body, collarbone, shirt, short_sleeves, white_sailor_collar, ;d, one_eye_closed, simple_background, white_background, :d, claw_pose, thick_eyebrows |
| 7 | 5 |  |  |  |  |  | 1girl, blush, erune, hair_flower, hat, looking_at_viewer, navel, official_alternate_costume, sailor_collar, sleeveless, solo, white_headwear, blue_shorts, short_shorts, simple_background, white_background, :d, bare_shoulders, midriff, open_mouth, white_belt, wrist_scrunchie, thick_eyebrows, white_jacket |
| 8 | 9 |  |  |  |  |  | 1girl, blue_shorts, hair_flower, hat, looking_at_viewer, midriff, navel, official_alternate_costume, open_mouth, sailor_collar, smile, solo, white_headwear, belt, blush, erune, wrist_scrunchie, blue_sky, day, outdoors, water, cloud, short_shorts, sleeveless_jacket, crop_top, one_eye_closed, wading |
| 9 | 6 |  |  |  |  |  | 1girl, blush, erune, hetero, looking_at_viewer, open_mouth, penis, pussy, solo_focus, 1boy, pantyhose, spread_legs, torn_clothes, nipples, sex, sweat, tongue_out, vaginal, bar_censor, on_back, pubic_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | detached_sleeves | erune | katana | looking_at_viewer | shimenawa | solo | bare_shoulders | holding_sword | wide_sleeves | sheath | smile | open_mouth | simple_background | white_background | black_pantyhose | blush | pelvic_curtain | japanese_clothes | pantyhose | fang | :d | claw_pose | hair_flower | kimono | nail_polish | obi | red_nails | petals | upper_body | long_sleeves | oil-paper_umbrella | white_flower | red_neckerchief | collarbone | shirt | short_sleeves | white_sailor_collar | ;d | one_eye_closed | thick_eyebrows | hat | navel | official_alternate_costume | sailor_collar | sleeveless | white_headwear | blue_shorts | short_shorts | midriff | white_belt | wrist_scrunchie | white_jacket | belt | blue_sky | day | outdoors | water | cloud | sleeveless_jacket | crop_top | wading | hetero | penis | pussy | solo_focus | 1boy | spread_legs | torn_clothes | nipples | sex | sweat | tongue_out | vaginal | bar_censor | on_back | pubic_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:--------|:---------|:--------------------|:------------|:-------|:-----------------|:----------------|:---------------|:---------|:--------|:-------------|:--------------------|:-------------------|:------------------|:--------|:-----------------|:-------------------|:------------|:-------|:-----|:------------|:--------------|:---------|:--------------|:------|:------------|:---------|:-------------|:---------------|:---------------------|:---------------|:------------------|:-------------|:--------|:----------------|:----------------------|:-----|:-----------------|:-----------------|:------|:--------|:-----------------------------|:----------------|:-------------|:-----------------|:--------------|:---------------|:----------|:-------------|:------------------|:---------------|:-------|:-----------|:------|:-----------|:--------|:--------|:--------------------|:-----------|:---------|:---------|:--------|:--------|:-------------|:-------|:--------------|:---------------|:----------|:------|:--------|:-------------|:----------|:-------------|:----------|:-------------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | 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 | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | | X | | X | | X | | | | | | X | X | X | | X | | | | | X | X | X | | | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | 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 | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 9 | 6 |  |  |  |  |  | X | | X | | X | | | | | | | | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
ashokpoudel/nepali-english-translation-dataset | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
- name: en
dtype: string
- name: np
dtype: string
splits:
- name: train
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download_size: 1890016055
dataset_size: 4034674546
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ohsuz/DACON_10000 | ---
dataset_info:
features:
- name: id
dtype: string
- name: input
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- name: output
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splits:
- name: train
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download_size: 2168831
dataset_size: 6257152
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
reralle/s-f-c | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': arabic
'1': dutch
'2': french
'3': korean
'4': mandarin
'5': portuguese
'6': russian
'7': spanish
'8': uk
'9': usa
splits:
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num_examples: 1516
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num_examples: 60
- name: validation
num_bytes: 51955368.0
num_examples: 60
download_size: 1229069272
dataset_size: 1439657469.632
---
# Dataset Card for "s-f-c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pcuenq/CelebA-faces-cropped-128-encoded | ---
dataset_info:
features:
- name: latents
sequence: float32
splits:
- name: test
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num_examples: 10130
- name: train
num_bytes: 789122900
num_examples: 192469
download_size: 843386957
dataset_size: 830655900
---
# Dataset Card for "CelebA-faces-cropped-128-encoded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zhan1993/x_positive_negative_expert | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: task_eval_on
dtype: string
- name: positive_expert_name
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- name: negative_expert_name
dtype: string
splits:
- name: train
num_bytes: 2089369
num_examples: 24005
download_size: 182622
dataset_size: 2089369
---
# Dataset Card for "x_positive_negative_expert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/squad_qa_no_id_v5_full_recite_ans_sent_last_permute_rerun | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
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dtype: string
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dtype: string
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num_examples: 4778
- name: validation
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num_examples: 300
download_size: 1573275
dataset_size: 8395098.88449685
---
# Dataset Card for "squad_qa_no_id_v5_full_recite_ans_sent_last_permute_rerun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Helsinki-NLP/un_pc | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- en
- es
- fr
- ru
- zh
license: other
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: united-nations-parallel-corpus
pretty_name: United Nations Parallel Corpus
config_names:
- ar-en
- ar-es
- ar-fr
- ar-ru
- ar-zh
- en-es
- en-fr
- en-ru
- en-zh
- es-fr
- es-ru
- es-zh
- fr-ru
- fr-zh
- ru-zh
dataset_info:
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features:
- name: translation
dtype:
translation:
languages:
- ar
- en
splits:
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dataset_size: 8039673899
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languages:
- ar
- es
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languages:
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data_files:
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path: ar-es/train-*
- config_name: ar-fr
data_files:
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path: ar-fr/train-*
- config_name: ar-ru
data_files:
- split: train
path: ar-ru/train-*
- config_name: ar-zh
data_files:
- split: train
path: ar-zh/train-*
- config_name: en-es
data_files:
- split: train
path: en-es/train-*
- config_name: en-fr
data_files:
- split: train
path: en-fr/train-*
- config_name: en-ru
data_files:
- split: train
path: en-ru/train-*
- config_name: en-zh
data_files:
- split: train
path: en-zh/train-*
- config_name: es-fr
data_files:
- split: train
path: es-fr/train-*
- config_name: es-ru
data_files:
- split: train
path: es-ru/train-*
- config_name: es-zh
data_files:
- split: train
path: es-zh/train-*
- config_name: fr-ru
data_files:
- split: train
path: fr-ru/train-*
- config_name: fr-zh
data_files:
- split: train
path: fr-zh/train-*
- config_name: ru-zh
data_files:
- split: train
path: ru-zh/train-*
---
# Dataset Card for United Nations Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://opus.nlpl.eu/UNPC/corpus/version/UNPC
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** https://aclanthology.org/L16-1561/
- **Leaderboard:** [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)
### Dataset Summary
The United Nations Parallel Corpus is the first parallel corpus composed from United Nations documents published by the original data creator.
The parallel corpus consists of manually translated UN documents from the last 25 years (1990 to 2014)
for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish.
The corpus is freely available for download under a liberal license.
### Supported Tasks and Leaderboards
The underlying task is machine translation.
### Languages
The six official UN languages: Arabic, Chinese, English, French, Russian, and Spanish.
## 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
https://conferences.unite.un.org/UNCORPUS/#disclaimer
The following disclaimer, an integral part of the United Nations Parallel Corpus, shall be respected with regard to the Corpus (no other restrictions apply):
- The United Nations Parallel Corpus is made available without warranty of any kind, explicit or implied. The United Nations specifically makes no warranties or representations as to the accuracy or completeness of the information contained in the United Nations Corpus.
- Under no circumstances shall the United Nations be liable for any loss, liability, injury or damage incurred or suffered that is claimed to have resulted from the use of the United Nations Corpus. The use of the United Nations Corpus is at the user's sole risk. The user specifically acknowledges and agrees that the United Nations is not liable for the conduct of any user. If the user is dissatisfied with any of the material provided in the United Nations Corpus, the user's sole and exclusive remedy is to discontinue using the United Nations Corpus.
- When using the United Nations Corpus, the user must acknowledge the United Nations as the source of the information. For references, please cite this reference: Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel Corpus, Language Resources and Evaluation (LRECโ16), Portoroลพ, Slovenia, May 2016.
- Nothing herein shall constitute or be considered to be a limitation upon or waiver, express or implied, of the privileges and immunities of the United Nations, which are specifically reserved.
### Citation Information
```
@inproceedings{ziemski-etal-2016-united,
title = "The {U}nited {N}ations Parallel Corpus v1.0",
author = "Ziemski, Micha{\\l} and
Junczys-Dowmunt, Marcin and
Pouliquen, Bruno",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://www.aclweb.org/anthology/L16-1561",
pages = "3530--3534",
abstract = "This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator. The parallel corpus presented consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. The corpus is freely available for download under a liberal license. Apart from the pairwise aligned documents, a fully aligned subcorpus for the six official UN languages is distributed. We provide baseline BLEU scores of our Moses-based SMT systems trained with the full data of language pairs involving English and for all possible translation directions of the six-way subcorpus.",
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
iKING-ROC/TransGPT-sft | ---
license: apache-2.0
---
|
gryffindor-ISWS/prompts_wiki_fictional_characters_raw_data_with_image | ---
license: gpl-3.0
---
|
Melricflash/CW_MedAbstracts | ---
license: apache-2.0
---
|
BigScienceBiasEval/crows_pairs_multilingual | ---
license: cc-by-sa-4.0
language:
- en
- fr
---
Original from https://gitlab.inria.fr/french-crows-pairs/acl-2022-paper-data-and-code/-/tree/main/.
# Data Statement for CrowS-Pairs-fr
> **How to use this document:**
> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.
> For full details, the best source is the original Data Statements paper, here: https://www.aclweb.org/anthology/Q18-1041/ .
> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as "DATASTATEMENT.md". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.
> Only blockquoted content should be deleted; the final about statement should be left intact.
Data set name: Crows-Pairs-fr
Citation (if available): Nรฉvรฉol A, Dupont Y, Bezanรงon J, Fort K. French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics - ACL 2022
Data set developer(s): Aurรฉlie Nรฉvรฉol, Yoann Dupont, Julien Bezanรงon, Karรซn Fort
Data statement author(s): Aurรฉlie Nรฉvรฉol, Yoann Dupont
Others who contributed to this document: N/A
License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).
## A. CURATION RATIONALE
> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.
The French part of the corpus was built by first translating the original 1,508 sentence pairs of the English corpus into French.
We then adapted the crowdsourcing method described by [Nangia et al. (2020)](https://arxiv.org/pdf/2010.00133) to collect additional sentences expressing a stereotype relevant to the French socio-cultural environment. Data collection is implemented through LanguageARC [(Fiumara et al., 2020)](https://www.aclweb.org/anthology/2020.cllrd-1.1.pdf), a citizen science platform supporting the development of language resources dedicated to social improvement. We created a LanguageARC project (https://languagearc.com/projects/19) to collect these additional sentences. Participants were asked to submit a statement that expressed a stereotype in French along with a selection of ten bias types: the nine bias types offered in CrowS-Pairs and the additional category _other_. We collected 210 additional sentences this way.
## B. LANGUAGE VARIETY/VARIETIES
> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., "English as spoken in Palo Alto, California", or "Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin").
* BCP-47 language tags: fr-FR
* Language variety description: French spoken by native French people from metropolitan France.
## C. CONTRIBUTOR DEMOGRAPHIC
> ## C. SPEAKER DEMOGRAPHIC
> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include:
N/A
## D. ANNOTATOR DEMOGRAPHIC
> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own โsocial addressโ influences their experience with language and thus their perception of what they are annotating. Specifications include:
Participants to the collection project were recruited through calls for volunteers posted to social media and mailing lists in the French research community.
## E. SPEECH SITUATION
N/A
## F. TEXT CHARACTERISTICS
> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.
Collected data is a collection of offensive stereotyped statements in French, they might be upsetting.
Along these stereotyped statements are paired anti-stereotyped statements.
## G. RECORDING QUALITY
N/A
## H. OTHER
> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.
## I. PROVENANCE APPENDIX
Examples were gathered using the LanguageArc site and by creating a dedicated project: https://languagearc.com/projects/19
## About this document
A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.
Data Statements are from the University of Washington. Contact: [datastatements@uw.edu](mailto:datastatements@uw.edu). This document template is licensed as [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
This version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the [2020 LREC workshop on Data Statements](https://sites.google.com/uw.edu/data-statements-for-nlp/), by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski.
|
andersonbcdefg/reward-modeling-long-tokenized | ---
dataset_info:
features:
- name: preferred_input_ids
sequence: int64
- name: preferred_attention_masks
sequence: int64
- name: dispreferred_input_ids
sequence: int64
- name: dispreferred_attention_masks
sequence: int64
splits:
- name: train
num_bytes: 17170494224
num_examples: 261937
download_size: 437217639
dataset_size: 17170494224
---
# Dataset Card for "reward-modeling-long-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mirza-92/sas_mistral | ---
license: apache-2.0
---
|
aladaf/homo-silicus-unboxing | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
pretty_name: unboxing
size_categories:
- 10K<n<100K
--- |
codesignal/tsla-historic-prices | ---
license: cc-by-4.0
language:
- en
tags:
- finance
pretty_name: TSLA Stock Historic Prices
size_categories:
- 1K<n<10K
--- |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_74 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1274242448.0
num_examples: 250244
download_size: 1300539126
dataset_size: 1274242448.0
---
# Dataset Card for "chunk_74"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
somosnlp/medical_en_es_formato_chatML_Gemma | ---
language:
- es
- en
size_categories:
- 10K<n<100K
task_categories:
- text-classification
dataset_info:
features:
- name: combination
dtype: string
- name: formatted_text
dtype: string
- name: len_token_gemma
dtype: int64
splits:
- name: train
num_bytes: 57174914
num_examples: 16078
download_size: 27891712
dataset_size: 57174914
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- medical
dataset:
- somosnlp/medical_bilingual_en_es
---
## 1. Distribucion de tokens del dataset.
La imagen muestra un histograma que representa la distribuciรณn de la longitud de tokens en un conjunto de entrenamiento filtrado para un modelo de procesamiento de lenguaje natural (NLP). El eje horizontal, titulado "Longitud de Tokens", muestra el rango de la cantidad de tokens por instancia de datos, que va de entre Mรญnimo: 97, Mรกximo: 2048 tokens. El eje vertical, titulado "Frecuencia", indica el nรบmero de instancias que caen dentro de cada intervalo de longitud de tokens.
en el grafico se evidencia un pico aparente alrededor de los 500 a 750 tokens. La frecuencia disminuye progresivamente para longitudes mayores de 1000 tokens, indicando que hay menos instancias de datos con una longitud tan extensa.
Esta distribuciรณn es รบtil para comprender el tamaรฑo tรญpico de las entradas de datos con las que el modelo trabajarรก mรกs eficientemente, y sugiere que el modelo estรก optimizado o ajustado para manejar preferentemente textos de una longitud intermedia, lo cual es importante para ajustar los parรกmetros de procesamiento y mejorar la inferencia del modelo.
<div style="display: flex; justify-content: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/7uD91cnB_H-Cup8u40dWR.png" style="width: 50%; max-height: 550px;">
</div>
## 2. Formato convertido a chatml con tokens de gemma.
Se realizo combinatorias de input y output: [[en,en], [en,es], [es,en], [es,es]]<br>
Con esto se el da el poder a el usuario desde el system para tener el control de sus respuestas.
```
<bos><start_of_turn>system
You are a helpful AI assistant.
Responde en formato json.
Eres un agente experto en medicina.
Lista de codigos linguisticos disponibles: ["en", "en"]<end_of_turn>
<start_of_turn>user
CHIEF COMPLAINT:, Right shoulder pain.,HISTORY OF PRESENT PROBLEM:<end_of_turn>
<start_of_turn>model
{
"description": "Consultation for right shoulder pain.",
"medical_specialty": "Consult - History and Phy.",
"principal_diagnostic": "Shoulder Pain Consult"
}<end_of_turn><eos>
```
```
<bos><start_of_turn>system
You are a helpful AI assistant.
Responde en formato json.
Eres un agente experto en medicina.
Lista de codigos linguisticos disponibles: ["en", "es"]<end_of_turn>
<start_of_turn>user
CHIEF COMPLAINT:, Left wrist pain.,HISTORY OF PRESENT PROBLEM:,<end_of_turn>
<start_of_turn>model
{
"description": "Consulta por dolor de muรฑeca.",
"medical_specialty": "Quiroprรกctica",
"principal_diagnostic": "Dolor de muรฑeca"
}<end_of_turn><eos>
```
```
<bos><start_of_turn>system
You are a helpful AI assistant.
Responde en formato json.
Eres un agente experto en medicina.
Lista de codigos linguisticos disponibles: ["es", "en"]<end_of_turn>
<start_of_turn>user
MOTIVO DE CONSULTA:, Dolor en la muรฑeca izquierda.,HISTORIA DEL PROBLEMA ACTUAL:<end_of_turn>
<start_of_turn>model
{
"description": "Consultation for wrist pain.",
"medical_specialty": "Chiropractic",
"principal_diagnostic": "Wrist Pain"
}<end_of_turn><eos>
```
```
<bos><start_of_turn>system
You are a helpful AI assistant.
Responde en formato json.
Eres un agente experto en medicina.
Lista de codigos linguisticos disponibles: ["en", "en"]<end_of_turn>
<start_of_turn>user
INDICATIONS: ,Chest pain.,STRESS TECHNIQUE:,<end_of_turn>
<start_of_turn>model
{
"description": "Thallium stress test for chest pain.",
"medical_specialty": "Cardiovascular / Pulmonary",
"principal_diagnostic": "Stress Test Thallium"
}<end_of_turn><eos>
```
## 3. Perfil.
```
https://huggingface.co/NickyNicky
```
## 4. Conjunto de datos extraido de.
```
https://huggingface.co/datasets/somosnlp/medical_bilingual_en_es
```
## 5. modelo entrenado con el conjunto de datos.
```
https://huggingface.co/somosnlp/Sam_Diagnostic
``` |
liuyanchen1015/MULTI_VALUE_mnli_it_is_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: 12573
num_examples: 52
- name: dev_mismatched
num_bytes: 14842
num_examples: 47
- name: test_matched
num_bytes: 11150
num_examples: 51
- name: test_mismatched
num_bytes: 10381
num_examples: 44
- name: train
num_bytes: 550494
num_examples: 2321
download_size: 338157
dataset_size: 599440
---
# Dataset Card for "MULTI_VALUE_mnli_it_is_referential"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zetavg/wikipedia_random_page_summaries_zh_tw_100k | ---
dataset_info:
features:
- name: page_title
dtype: string
- name: page_summary
dtype: string
splits:
- name: train
num_bytes: 39701385
num_examples: 99977
download_size: 29305208
dataset_size: 39701385
---
# Dataset Card for "wikipedia_random_page_summaries_zh_tw_100k"
`page_title` ๆฏ็ถญๅบ็พ็งๅๅง็้ ้ขๅ็จฑ๏ผๅ ๆญคๅฏ่ฝๆฏ็ฐก้ซไธญๆใ`page_summary` ๅไธๅพๆฏๅฐ็ฃๆญฃ้ซ็ๆฌใ
ไฝฟ็จไบ [vinta/pangu](https://github.com/vinta/pangu.js) ไพ็ขบไฟไธญ่ฑๆไน้้ฝๆๅ ไธ็ฉบๆ ผใ
็ฑ https://github.com/zetavg/LLM-Research/blob/3b79836/Wikipedia_Random_Page_Summaries_Dataset_Generator.ipynb ็ข็ใ
|
BNNT/mozi_IP_instructions | ---
license: apache-2.0
---
|
AgentWaller/german-oasst1-qlora-format | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12508931
num_examples: 9843
- name: validation
num_bytes: 659811
num_examples: 517
download_size: 7406128
dataset_size: 13168742
---
|
hojzas/test | ---
license: apache-2.0
---
|
dkshjn/processed_truthy-v3 | ---
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: system
dtype: string
- name: prompt
dtype: string
- name: rejected
dtype: string
- name: formatted_chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: formatted_rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 2777217
num_examples: 1016
download_size: 1168067
dataset_size: 2777217
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "processed_truthy-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
player1537/wizard-vicuna-unfiltered-summarized-70k | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 67191553
num_examples: 34598
download_size: 37189054
dataset_size: 67191553
---
# Dataset Card for "wizard-vicuna-unfiltered-summarized-70k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Abdelkareem/rwkv_articles_30_000 | ---
license: apache-2.0
---
|
Dstycoon/trial2 | ---
dataset_info:
features:
- name: input
sequence: string
- name: generation_model
sequence: string
- name: generation_prompt
list:
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_generation_responses
sequence: string
- name: instructions
sequence:
sequence: string
splits:
- name: train
num_bytes: 11039
num_examples: 2
download_size: 25822
dataset_size: 11039
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zolak/twitter_dataset_78_1713155657 | ---
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: 536417
num_examples: 1389
download_size: 268312
dataset_size: 536417
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ZiHDeng/hf-ny8-v1 | ---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1456330
num_examples: 7660
download_size: 149993
dataset_size: 1456330
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kyujinpy/KOR-OpenOrca-Platypus | ---
language:
- ko
license: cc-by-nc-4.0
size_categories:
- 10K<n<50K
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 78588418
num_examples: 46558
download_size: 39656100
dataset_size: 78588418
---
# KOR-OpenOrca-Platypus
- OpenOrca-Ko + KOpen-platypus
- ๋ฐ์ดํฐ์
์ด์ฉํ์
์ ๋ชจ๋ธ์ด๋ ๋ฐ์ดํฐ์
์ ๋ง๋์ค ๋, ๊ฐ๋จํ ์ถ์ฒ ํ๊ธฐ๋ฅผ ํด์ฃผ์ ๋ค๋ฉด ์ฐ๊ตฌ์ ํฐ ๋์์ด ๋ฉ๋๋ค๐ญ๐ญ
## KOpen-platpyus
Repo: [KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus)
- ๊ณ ํ์ง ํ๊ตญ์ด ๋ฐ์ดํฐ์
1. ์ฝ๋์ ์ฃผ์์ ๊ทธ๋๋ก ์ ์งํ๊ณ , ์ค๋ช
๋ถ๋ถ๋ง ํ๊ตญ์ด๋ก ์์
2. 1๋ฒ๊ณผ ๋๋ถ์ด์, Python, Java, Cpp, xml ๋ฑ๋ฑ ๊ฒฐ๊ณผ๋ค์ ์ ๋ถ ๊ธฐ์กด์ ๋ฐ์ดํฐ ํํ๋ก ์ต๋ํ ๋ณด์กด
3. ๋จ์ผ ์ซ์์ ์์ด๋ ๋ณธ๋์ ๊ฒฐ๊ณผ ๊ทธ๋๋ก ๊ฐ์ ธ์ด
4. DeepL Pro ๋ฒ์ญ ๊ฒฐ๊ณผ ์ค ๋ฏธ์์ฑ ๋ณ์ญ ๊ฒฐ๊ณผ ์ง์ ์์ (์๋ฅผ ๋ค๋ฉด, '[...]'๊ฐ ํฌํจ๋์ด ์์)
5. DeepL Pro ๋ฒ์ญ ๊ฒฐ๊ณผ๊ฐ ๋ณธ๋์ ๋ฐ์ดํฐ์ ๋นํด ๊ธ์์๊ฐ 50% ์ดํ๋ก ๋ฎ์ผ๋ฉด, ๋ฒ์ญ ๊ฒฐ๊ณผ ์์
6. ๋ฒ์ญํ๊ณ ์ ํ๋ ๊ธ์์๊ฐ 1500์ ์ด์์ผ ๊ฒฝ์ฐ, API๋ก ๋ณ๊ฒฝํด์ ๋ฒ์ญ
7. ๊ณ ์ ๋ช
์ฌ๋ ์ต๋ํ ์ ์งํจ
> Post-processing ์์
๋ด์ฉ
## OpenOrca-Ko
Repo: [OpenOrca-Ko](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO)
1. NIV // 1571๊ฐ
2. FLAN // 9434๊ฐ
3. T0 // 6351๊ฐ
4. CoT // 2117๊ฐ
5. KoCoT // 2159๊ฐ
> Dataset ๊ตฌ์ฑ
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [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)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>๐ The OpenOrca Dataset! ๐</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` |
Intel/textual_inversion_dicoo_dfq | ---
license: apache-2.0
---
# Distillation for quantization on Textual Inversion models to personalize text2image with Intelยฎ Neural Compressor
<p float="left">
<img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/FP32.png" width = "300" height = "300" alt="FP32" align=center />
<img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/INT8.png" width = "300" height = "300" alt="INT8" align=center />
</p>
Image on the top is generated from the FP32 finetuned stable diffusion model, bottom image is generated from the INT8 stable diffusion model which is quantized from the FP32 model by distillation for quantization approach.
<br>
Please refer to this <a href="https://github.com/intel/neural-compressor/tree/master/examples/pytorch/diffusion_model/diffusers/textual_inversion/distillation_for_quantization">example</a> of Intelยฎ Neural Compressor for more detail. |
AdapterOcean/med_alpaca_standardized_cluster_58_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 13148251
num_examples: 7216
download_size: 7354631
dataset_size: 13148251
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_58_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
suthawadee/receipt_th_1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 14696947.0
num_examples: 80
- name: validation
num_bytes: 1693188.0
num_examples: 10
- name: test
num_bytes: 1855175.0
num_examples: 10
download_size: 18156103
dataset_size: 18245310.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay | ---
pretty_name: Evaluation run of anhnv125/pygmalion-6b-roleplay
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [anhnv125/pygmalion-6b-roleplay](https://huggingface.co/anhnv125/pygmalion-6b-roleplay)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T05:57:52.375499](https://huggingface.co/datasets/open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay/blob/main/results_2023-09-17T05-57-52.375499.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\": 0.002936241610738255,\n\
\ \"em_stderr\": 0.0005541113054709947,\n \"f1\": 0.05561765939597344,\n\
\ \"f1_stderr\": 0.0013547337231371388,\n \"acc\": 0.3190247209594698,\n\
\ \"acc_stderr\": 0.008257334480912119\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709947,\n\
\ \"f1\": 0.05561765939597344,\n \"f1_stderr\": 0.0013547337231371388\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.011372251705837756,\n \
\ \"acc_stderr\": 0.002920666198788722\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6266771902131019,\n \"acc_stderr\": 0.013594002763035516\n\
\ }\n}\n```"
repo_url: https://huggingface.co/anhnv125/pygmalion-6b-roleplay
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_01T14_17_43.702617
path:
- '**/details_harness|arc:challenge|25_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T05_57_52.375499
path:
- '**/details_harness|drop|3_2023-09-17T05-57-52.375499.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T05-57-52.375499.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T05_57_52.375499
path:
- '**/details_harness|gsm8k|5_2023-09-17T05-57-52.375499.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T05-57-52.375499.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hellaswag|10_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T14:17:43.702617.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-01T14:17:43.702617.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-01T14:17:43.702617.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T05_57_52.375499
path:
- '**/details_harness|winogrande|5_2023-09-17T05-57-52.375499.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T05-57-52.375499.parquet'
- config_name: results
data_files:
- split: 2023_08_01T14_17_43.702617
path:
- results_2023-08-01T14:17:43.702617.parquet
- split: 2023_09_17T05_57_52.375499
path:
- results_2023-09-17T05-57-52.375499.parquet
- split: latest
path:
- results_2023-09-17T05-57-52.375499.parquet
---
# Dataset Card for Evaluation run of anhnv125/pygmalion-6b-roleplay
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/anhnv125/pygmalion-6b-roleplay
- **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 [anhnv125/pygmalion-6b-roleplay](https://huggingface.co/anhnv125/pygmalion-6b-roleplay) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T05:57:52.375499](https://huggingface.co/datasets/open-llm-leaderboard/details_anhnv125__pygmalion-6b-roleplay/blob/main/results_2023-09-17T05-57-52.375499.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": 0.002936241610738255,
"em_stderr": 0.0005541113054709947,
"f1": 0.05561765939597344,
"f1_stderr": 0.0013547337231371388,
"acc": 0.3190247209594698,
"acc_stderr": 0.008257334480912119
},
"harness|drop|3": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054709947,
"f1": 0.05561765939597344,
"f1_stderr": 0.0013547337231371388
},
"harness|gsm8k|5": {
"acc": 0.011372251705837756,
"acc_stderr": 0.002920666198788722
},
"harness|winogrande|5": {
"acc": 0.6266771902131019,
"acc_stderr": 0.013594002763035516
}
}
```
### 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] |
DynamicSuperb/EmotionRecognition_MultimodalEmotionlinesDataset | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: instruction
dtype: string
- name: label
dtype: string
splits:
- name: test
num_bytes: 58838156.0
num_examples: 200
download_size: 57671696
dataset_size: 58838156.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "emotion_recognition_multimodal_emotionlines_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
katielink/synapse_test_dataset | ---
license: other
tags:
- synapse
- biology
---
# Dataset Card for Synapse Test Dataset
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Point of Contact:**
### Dataset Summary
This is a test dataset to highlight the functionality of a dataset script with a custom loading function.
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).
### 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] |
ShieldX/manovyadh-3.5k | ---
dataset_info:
features:
- name: Context
dtype: string
- name: Response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 10684088
num_examples: 3512
download_size: 5161163
dataset_size: 10684088
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- text-classification
- question-answering
- conversational
- text-generation
language:
- en
tags:
- biology
- medical
pretty_name: ManoVyadh
size_categories:
- 1K<n<10K
--- |
sled-umich/2D-ATOMS | ---
task_categories:
- zero-shot-classification
language:
- en
tags:
- LLM
- Theory-Of-Mind
size_categories:
- 1K<n<10K
license: mit
---
# 2D-ATOMS: 2D Abilities in Theory of Mind Space dataset
Official dataset for [**Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models**](https://arxiv.org/abs/2310.19619). Ziqiao Ma, Jacob Sansom, Run Peng, Joyce Chai. EMNLP Findings, 2023.
## Overview
We introduce **2D-ATOMS** dataset, a novel text-based dataset that evaluates a machine's reasoning process under a situated theory-of-mind setting.
Our dataset includes 9 different ToM evaluation tasks for each mental state under ATOMS[1], and 1 reality-checking task to test LLMsโ understanding of the world. It is important to acknowledge that our experiment serves as a proof of concept and does not aim to cover the entire spectrum of machine ToM, as our case studies are far from being exhaustive or systematic. Here we release the zero-shot version of our dataset, which is used in our paper.
If you find our work useful, please give us credit by citing:
```bibtex
@inproceedings{ma2023towards,
title={Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models},
author={Ma, Ziqiao and Sansom, Jacob and Peng, Run and Chai, Joyce},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
year={2023}
}
```
## Download
```python
from datasets import load_dataset
dataset = load_dataset("sled-umich/2D-ATOMS")
```
## Reference
[1] C. Beaudoin, ร. Leblanc, C. Gagner, and M. H. Beauchamp, โSystematic review and inventory of theory of mind measures for young childrenโ, Frontiers in psychology, vol. 10, p. 2905, 2020. |
lopezONE/wenstei | ---
license: mit
---
|
OdiaGenAI/health_hindi_200 | ---
task_categories:
- question-answering
language:
- hi
tags:
- medical
size_categories:
- n<1K
---
Contributors: -
- Sonal Khosla |
Flavinhouaua2022/VALDEMIR | ---
license: openrail
---
|
priyank-m/text_recognition_en_zh | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 24066869565.924767
num_examples: 1447547
- name: val
num_bytes: 49945762.89211632
num_examples: 3000
- name: test
num_bytes: 49961758.89211632
num_examples: 3000
download_size: 5513763538
dataset_size: 24166777087.709
---
# Dataset Card for "text_recognition_en_zh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/spuria_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of spuria/ในใใชใข/็ฉบๆ (Arknights)
This is the dataset of spuria/ในใใชใข/็ฉบๆ (Arknights), containing 31 images and their tags.
The core tags of this character are `short_hair, halo, blue_hair, breasts, hair_between_eyes, grey_eyes, wings`, 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 | 31 | 51.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spuria_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 31 | 41.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spuria_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 73 | 80.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spuria_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/spuria_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 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, solo, upper_body, simple_background, smile, tank_top, collarbone, earrings, white_background, bare_shoulders, blue_eyes, cleavage, crop_top, grey_jacket, long_sleeves, off_shoulder, open_jacket, sports_bra, white_gloves |
| 1 | 7 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, smile, solo, white_gloves, holding_gun, white_pants, collarbone, cowboy_shot, crop_top, grey_jacket, white_background, closed_mouth, hand_up, handgun, simple_background, white_tank_top, belt_pouch, detached_wings, energy_wings, open_jacket, shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | upper_body | simple_background | smile | tank_top | collarbone | earrings | white_background | bare_shoulders | blue_eyes | cleavage | crop_top | grey_jacket | long_sleeves | off_shoulder | open_jacket | sports_bra | white_gloves | holding_gun | white_pants | cowboy_shot | closed_mouth | hand_up | handgun | white_tank_top | belt_pouch | detached_wings | energy_wings | shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------|:--------------------|:--------|:-----------|:-------------|:-----------|:-------------------|:-----------------|:------------|:-----------|:-----------|:--------------|:---------------|:---------------|:--------------|:-------------|:---------------|:--------------|:--------------|:--------------|:---------------|:----------|:----------|:-----------------|:-------------|:-----------------|:---------------|:--------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | 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 | X | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_AA051610__A12P | ---
pretty_name: Evaluation run of AA051610/A12P
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AA051610/A12P](https://huggingface.co/AA051610/A12P) 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_AA051610__A12P\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-12T06:57:37.769631](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__A12P/blob/main/results_2023-12-12T06-57-37.769631.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\": 0.6960316046766869,\n\
\ \"acc_stderr\": 0.030603322312658773,\n \"acc_norm\": 0.7008390046754603,\n\
\ \"acc_norm_stderr\": 0.031195836387238177,\n \"mc1\": 0.4565483476132191,\n\
\ \"mc1_stderr\": 0.01743728095318369,\n \"mc2\": 0.6222123218100973,\n\
\ \"mc2_stderr\": 0.015308802289329178\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.014144193471893454,\n\
\ \"acc_norm\": 0.64419795221843,\n \"acc_norm_stderr\": 0.01399057113791876\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6210914160525791,\n\
\ \"acc_stderr\": 0.004841238763529372,\n \"acc_norm\": 0.8232423819956184,\n\
\ \"acc_norm_stderr\": 0.0038068384481617415\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8355263157894737,\n \"acc_stderr\": 0.030167533468632723,\n\
\ \"acc_norm\": 0.8355263157894737,\n \"acc_norm_stderr\": 0.030167533468632723\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\
\ \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \
\ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7320754716981132,\n \"acc_stderr\": 0.027257260322494845,\n\
\ \"acc_norm\": 0.7320754716981132,\n \"acc_norm_stderr\": 0.027257260322494845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\
\ \"acc_stderr\": 0.034370793441061344,\n \"acc_norm\": 0.7847222222222222,\n\
\ \"acc_norm_stderr\": 0.034370793441061344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.59,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\"\
: 0.59,\n \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\
\ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\
\ \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396264,\n\
\ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396264\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7319148936170212,\n \"acc_stderr\": 0.028957342788342343,\n\
\ \"acc_norm\": 0.7319148936170212,\n \"acc_norm_stderr\": 0.028957342788342343\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7103448275862069,\n \"acc_stderr\": 0.03780019230438015,\n\
\ \"acc_norm\": 0.7103448275862069,\n \"acc_norm_stderr\": 0.03780019230438015\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.5714285714285714,\n \"acc_stderr\": 0.025487187147859375,\n \"\
acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.025487187147859375\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8483870967741935,\n\
\ \"acc_stderr\": 0.02040261665441676,\n \"acc_norm\": 0.8483870967741935,\n\
\ \"acc_norm_stderr\": 0.02040261665441676\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5566502463054187,\n \"acc_stderr\": 0.03495334582162934,\n\
\ \"acc_norm\": 0.5566502463054187,\n \"acc_norm_stderr\": 0.03495334582162934\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\
\ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8737373737373737,\n \"acc_stderr\": 0.023664359402880215,\n \"\
acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.023664359402880215\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\
\ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7410256410256411,\n \"acc_stderr\": 0.022211106810061665,\n\
\ \"acc_norm\": 0.7410256410256411,\n \"acc_norm_stderr\": 0.022211106810061665\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8151260504201681,\n \"acc_stderr\": 0.025215992877954202,\n\
\ \"acc_norm\": 0.8151260504201681,\n \"acc_norm_stderr\": 0.025215992877954202\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\
acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8642201834862385,\n \"acc_stderr\": 0.01468690755634002,\n \"\
acc_norm\": 0.8642201834862385,\n \"acc_norm_stderr\": 0.01468690755634002\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5601851851851852,\n \"acc_stderr\": 0.0338517797604481,\n \"acc_norm\"\
: 0.5601851851851852,\n \"acc_norm_stderr\": 0.0338517797604481\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8627450980392157,\n\
\ \"acc_stderr\": 0.024152225962801588,\n \"acc_norm\": 0.8627450980392157,\n\
\ \"acc_norm_stderr\": 0.024152225962801588\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.8776371308016878,\n \"acc_stderr\": 0.021331741829746793,\n\
\ \"acc_norm\": 0.8776371308016878,\n \"acc_norm_stderr\": 0.021331741829746793\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7668161434977578,\n\
\ \"acc_stderr\": 0.028380391147094706,\n \"acc_norm\": 0.7668161434977578,\n\
\ \"acc_norm_stderr\": 0.028380391147094706\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804475,\n\
\ \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804475\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8347107438016529,\n \"acc_stderr\": 0.03390780612972776,\n \"\
acc_norm\": 0.8347107438016529,\n \"acc_norm_stderr\": 0.03390780612972776\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\
\ \"acc_stderr\": 0.035207039905179635,\n \"acc_norm\": 0.8425925925925926,\n\
\ \"acc_norm_stderr\": 0.035207039905179635\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8159509202453987,\n \"acc_stderr\": 0.03044677768797174,\n\
\ \"acc_norm\": 0.8159509202453987,\n \"acc_norm_stderr\": 0.03044677768797174\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6071428571428571,\n\
\ \"acc_stderr\": 0.046355501356099754,\n \"acc_norm\": 0.6071428571428571,\n\
\ \"acc_norm_stderr\": 0.046355501356099754\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\
\ \"acc_stderr\": 0.019875655027867457,\n \"acc_norm\": 0.8974358974358975,\n\
\ \"acc_norm_stderr\": 0.019875655027867457\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8876117496807152,\n\
\ \"acc_stderr\": 0.01129454135121655,\n \"acc_norm\": 0.8876117496807152,\n\
\ \"acc_norm_stderr\": 0.01129454135121655\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7658959537572254,\n \"acc_stderr\": 0.022797110278071128,\n\
\ \"acc_norm\": 0.7658959537572254,\n \"acc_norm_stderr\": 0.022797110278071128\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5027932960893855,\n\
\ \"acc_stderr\": 0.01672224059549172,\n \"acc_norm\": 0.5027932960893855,\n\
\ \"acc_norm_stderr\": 0.01672224059549172\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.024404394928087873,\n\
\ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.024404394928087873\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7491961414790996,\n\
\ \"acc_stderr\": 0.024619771956697168,\n \"acc_norm\": 0.7491961414790996,\n\
\ \"acc_norm_stderr\": 0.024619771956697168\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8024691358024691,\n \"acc_stderr\": 0.02215288992789896,\n\
\ \"acc_norm\": 0.8024691358024691,\n \"acc_norm_stderr\": 0.02215288992789896\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5531914893617021,\n \"acc_stderr\": 0.02965823509766691,\n \
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.02965823509766691\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5338983050847458,\n\
\ \"acc_stderr\": 0.012740853872949834,\n \"acc_norm\": 0.5338983050847458,\n\
\ \"acc_norm_stderr\": 0.012740853872949834\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940675,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940675\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.75,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.75,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.7181818181818181,\n \"acc_stderr\": 0.043091187099464585,\n\
\ \"acc_norm\": 0.7181818181818181,\n \"acc_norm_stderr\": 0.043091187099464585\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.763265306122449,\n\
\ \"acc_stderr\": 0.02721283588407315,\n \"acc_norm\": 0.763265306122449,\n\
\ \"acc_norm_stderr\": 0.02721283588407315\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.8706467661691543,\n \"acc_stderr\": 0.023729830881018512,\n\
\ \"acc_norm\": 0.8706467661691543,\n \"acc_norm_stderr\": 0.023729830881018512\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \"acc_norm\": 0.87,\n\
\ \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness|hendrycksTest-virology|5\"\
: {\n \"acc\": 0.572289156626506,\n \"acc_stderr\": 0.038515976837185335,\n\
\ \"acc_norm\": 0.572289156626506,\n \"acc_norm_stderr\": 0.038515976837185335\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8421052631578947,\n\
\ \"acc_stderr\": 0.027966785859160886,\n \"acc_norm\": 0.8421052631578947,\n\
\ \"acc_norm_stderr\": 0.027966785859160886\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.4565483476132191,\n \"mc1_stderr\": 0.01743728095318369,\n\
\ \"mc2\": 0.6222123218100973,\n \"mc2_stderr\": 0.015308802289329178\n\
\ },\n \"harness|winogrande|5\": {\n \"acc\": 0.7963693764798737,\n\
\ \"acc_stderr\": 0.011317798781626915\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.5329795299469295,\n \"acc_stderr\": 0.013742492794163425\n\
\ }\n}\n```"
repo_url: https://huggingface.co/AA051610/A12P
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_12_12T06_57_37.769631
path:
- '**/details_harness|arc:challenge|25_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|gsm8k|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hellaswag|10_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T06-57-37.769631.parquet'
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- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T06-57-37.769631.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T06-57-37.769631.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T06-57-37.769631.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- '**/details_harness|winogrande|5_2023-12-12T06-57-37.769631.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-12T06-57-37.769631.parquet'
- config_name: results
data_files:
- split: 2023_12_12T06_57_37.769631
path:
- results_2023-12-12T06-57-37.769631.parquet
- split: latest
path:
- results_2023-12-12T06-57-37.769631.parquet
---
# Dataset Card for Evaluation run of AA051610/A12P
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [AA051610/A12P](https://huggingface.co/AA051610/A12P) 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_AA051610__A12P",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-12T06:57:37.769631](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__A12P/blob/main/results_2023-12-12T06-57-37.769631.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": 0.6960316046766869,
"acc_stderr": 0.030603322312658773,
"acc_norm": 0.7008390046754603,
"acc_norm_stderr": 0.031195836387238177,
"mc1": 0.4565483476132191,
"mc1_stderr": 0.01743728095318369,
"mc2": 0.6222123218100973,
"mc2_stderr": 0.015308802289329178
},
"harness|arc:challenge|25": {
"acc": 0.6254266211604096,
"acc_stderr": 0.014144193471893454,
"acc_norm": 0.64419795221843,
"acc_norm_stderr": 0.01399057113791876
},
"harness|hellaswag|10": {
"acc": 0.6210914160525791,
"acc_stderr": 0.004841238763529372,
"acc_norm": 0.8232423819956184,
"acc_norm_stderr": 0.0038068384481617415
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8355263157894737,
"acc_stderr": 0.030167533468632723,
"acc_norm": 0.8355263157894737,
"acc_norm_stderr": 0.030167533468632723
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7320754716981132,
"acc_stderr": 0.027257260322494845,
"acc_norm": 0.7320754716981132,
"acc_norm_stderr": 0.027257260322494845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7847222222222222,
"acc_stderr": 0.034370793441061344,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.034370793441061344
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.44,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6242774566473989,
"acc_stderr": 0.036928207672648664,
"acc_norm": 0.6242774566473989,
"acc_norm_stderr": 0.036928207672648664
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.45098039215686275,
"acc_stderr": 0.04951218252396264,
"acc_norm": 0.45098039215686275,
"acc_norm_stderr": 0.04951218252396264
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7319148936170212,
"acc_stderr": 0.028957342788342343,
"acc_norm": 0.7319148936170212,
"acc_norm_stderr": 0.028957342788342343
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7103448275862069,
"acc_stderr": 0.03780019230438015,
"acc_norm": 0.7103448275862069,
"acc_norm_stderr": 0.03780019230438015
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.5714285714285714,
"acc_stderr": 0.025487187147859375,
"acc_norm": 0.5714285714285714,
"acc_norm_stderr": 0.025487187147859375
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677171,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677171
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8483870967741935,
"acc_stderr": 0.02040261665441676,
"acc_norm": 0.8483870967741935,
"acc_norm_stderr": 0.02040261665441676
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5566502463054187,
"acc_stderr": 0.03495334582162934,
"acc_norm": 0.5566502463054187,
"acc_norm_stderr": 0.03495334582162934
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8121212121212121,
"acc_stderr": 0.03050193405942914,
"acc_norm": 0.8121212121212121,
"acc_norm_stderr": 0.03050193405942914
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8737373737373737,
"acc_stderr": 0.023664359402880215,
"acc_norm": 0.8737373737373737,
"acc_norm_stderr": 0.023664359402880215
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.917098445595855,
"acc_stderr": 0.01989934131572178,
"acc_norm": 0.917098445595855,
"acc_norm_stderr": 0.01989934131572178
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7410256410256411,
"acc_stderr": 0.022211106810061665,
"acc_norm": 0.7410256410256411,
"acc_norm_stderr": 0.022211106810061665
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.028820884666253255,
"acc_norm": 0.337037037037037,
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"harness|hendrycksTest-international_law|5": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.5329795299469295,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## 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
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## 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. -->
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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. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
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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]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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mindcrafterjesse/Furries | ---
license: unknown
---
|
ai4bharat/IndicHeadlineGeneration | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicHeadlineGeneration
size_categories:
- 27K<n<341K
source_datasets:
- original for Hindi, and modified [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) for other languages.
task_categories:
- conditional-text-generation
task_ids:
- conditional-text-generation-other-headline-generation
---
# Dataset Card for "IndicHeadlineGeneration"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://indicnlp.ai4bharat.org/indicnlg-suite
- **Paper:** [IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://arxiv.org/abs/2203.05437)
- **Point of Contact:**
### Dataset Summary
IndicHeadlineGeneration is the news headline generation dataset released as part of IndicNLG Suite. Each
input document is paired with an output as title. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total
size of the dataset is 1.4M.
### Supported Tasks and Leaderboards
**Tasks:** Headline Generation
**Leaderboards:** Currently there is no Leaderboard for this dataset.
### Languages
- `Assamese (as)`
- `Bengali (bn)`
- `Gujarati (gu)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Marathi (mr)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`
## Dataset Structure
### Data Instances
One random example from the `hi` dataset is given below in JSON format.
```
{'id': '14',
'input': "เค
เคฎเฅเคฐเคฟเคเฅ เคธเคฟเคเคเคฐ เค
เคฐเคฟเคฏเคพเคจเคพ เคเฅเคฐเคพเคเคกเฅ เคเคพ เคจเคฏเคพ เคฎเฅเคฏเฅเคเคฟเค เคเคฒเฅเคฌเคฎ 'เคฅเฅเคเค เคฏเฅ เคจเฅเคเฅเคธเฅเค' เคฐเคฟเคฒเฅเค เคนเฅ เคเคฏเคพ เคนเฅเฅคเคเค เคฆเคฟเคจ เคชเคนเคฒเฅ เคนเฅ เคฐเคฟเคฒเฅเค เคนเฅเค เคเคธ เคเคพเคจเฅ เคเฅ เคฆเฅเคเคจเฅ เคตเคพเคฒเฅเค เคเฅ เคธเคเคเฅเคฏเคพ 37,663,702 เคชเคนเฅเคเค เคเค เคนเฅเฅคเคฏเฅเคเฅเคฏเฅเคฌ เคชเคฐ เค
เคชเคฒเฅเคก เคเคธ เคเคพเคจเฅ เคเฅ 24 เคเคเคเฅ เคเฅ เคญเฅเคคเคฐ 3.8 เคฎเคฟเคฒเคฟเคฏเคจ เคฒเฅเคเฅเค เคจเฅ เคชเคธเคเคฆ เคเคฟเคฏเคพ เคนเฅเฅคเค
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เคฎเฅเคฐเคฟเคเฅ เคธเคฟเคเคเคฐ เค
เคฐเคฟเคฏเคพเคจเคพ เคเฅเคฐเคพเคเคกเฅ เคเคพ เคจเคฏเคพ เคฎเฅเคฏเฅเคเคฟเค เคเคฒเฅเคฌเคฎ 'เคฅเฅเคเค เคฏเฅ เคจเฅเคเฅเคธเฅเค' เคฐเคฟเคฒเฅเค เคนเฅ เคเคฏเคพ เคนเฅเฅคเคเค เคฆเคฟเคจ เคชเคนเคฒเฅ เคนเฅ เคฐเคฟเคฒเฅเค เคนเฅเค เคเคธ เคเคพเคจเฅ เคเฅ เคฆเฅเคเคจเฅ เคตเคพเคฒเฅเค เคเฅ เคธเคเคเฅเคฏเคพ 37,663,702 เคชเคนเฅเคเค เคเค เคนเฅเฅคเคฏเฅเคเฅเคฏเฅเคฌ เคชเคฐ เค
เคชเคฒเฅเคก เคเคธ เคเคพเคจเฅ เคเฅ 24 เคเคเคเฅ เคเฅ เคญเฅเคคเคฐ 3.8 เคฎเคฟเคฒเคฟเคฏเคจ เคฒเฅเคเฅเค เคจเฅ เคชเคธเคเคฆ เคเคฟเคฏเคพ เคนเฅเฅคเคตเคนเฅเค เคเคธ เคตเฅเคกเคฟเคฏเฅ เคชเคฐ เคเคฎเฅเคเคเฅเคธ เคเฅ เคฌเคพเคขเคผ เค เคเค เคนเฅเฅคเคเคพเคจเฅ เคฎเฅเค เคฎเฅเคจ เคเคฐเฅเคฒเฅเคธ, เคฌเฅเคฐเคฟเคเค เคเค เคเคจ, เคฒเฅเคเคฒเฅ เคฌเฅเคฒเฅเคเคก เคเคฐ 13 เคเฅเคเคเค 30 เคเฅ เคเฅเค เคซเฅเคฎเคธ เคธเฅเคจเฅเคธ เคเฅ เคฆเคฟเคเคพเคฏเคพ เคเคฏเคพ เคนเฅเฅคเคเคพเคจเฅ เคฎเฅเค เคเฅเคฐเคฟเคธ เคเฅเคจเคฐ เคเคพ เคเฅเคฎเคฟเคฏเฅ เคญเฅ เคนเฅเฅคเคฌเคคเคพ เคฆเฅเค เค
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เคช เคเคฐ เคฒเคฟเคฏเคพเฅคเคชเคฐ เคฆเฅเคถ-เคตเคฟเคฆเฅเคถ เคเฅ เคคเคพเคเคพ เคเคฐ เคธเฅเคชเฅเคถเคฒ เคธเฅเคเฅเคฐเฅ เคชเคขเคผเคคเฅ เคนเฅเค เค
เคชเคจเฅ เคเคช เคเฅ เคฐเคเคฟเค เค
เคช-เคเฅ-เคกเฅเคเฅคเคเฅ เคฒเคฟเค เคเฅเคฒเคฟเค เคเคฐเฅเค เคธเคฟเคจเฅเคฎเคพ เคธเฅเคเฅเคถเคจ",
'target': 'เค
เคฐเคฟเคฏเคพเคจเคพ เคเฅเคฐเคพเคเคกเฅ เคเคพ เคจเคฏเคพ เคเคพเคจเคพ เคฐเคฟเคฒเฅเค, เคธเฅเคถเคฒ เคฎเฅเคกเคฟเคฏเคพ เคชเคฐ เคตเคพเคฏเคฐเคฒ',
'url': 'https://www.indiatv.in/entertainment/hollywood-ariana-grande-shatters-24-hour-views-record-612835'
}
```
### Data Fields
- `id (string)`: Unique identifier.
- `input (string)`: News article as input.
- `target (strings)`: Output as headline of the news article.
- `url (string)`: Source web link of the news article.
### Data Splits
Here is the number of samples in each split for all the languages.
Language | ISO 639-1 Code | Train | Dev | Test |
---------- | ---------- | ---------- | ---------- | ---------- |
Assamese | as | 29,631 | 14,592 | 14,808 |
Bengali | bn | 113,424 | 14,739 | 14,568 |
Gujarati | gu | 199,972 | 31,270 | 31,215 |
Hindi | hi | 208,221 | 44,738 | 44,514 |
Kannada | kn | 132,380 | 19,416 | 3,261 |
Malayalam | ml | 10,358 | 5,388 | 5,220 |
Marathi | mr | 114,042 | 14,253 | 14,340 |
Oriya | or | 58,225 | 7,484 | 7,137 |
Punjabi | pa | 48,441 | 6,108 | 6,086 |
Tamil | ta | 60,650 | 7,616 | 7,688 |
Telugu | te | 21,352 | 2,690 | 2,675 |
## Dataset Creation
### Curation Rationale
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
### Source Data
For hindi, web sources like [Dainik Bhaskar](https://www.bhaskar.com), [Naidunia](https://www.naidunia.com/), [NDTV](https://ndtv.in/), [Business Standard](https://hindi.business-standard.com/) and [IndiaTV](https://www.indiatv.in/). For other languages, modified [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) dataset.
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
### 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
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437",
```
### Contributions
[Detailed in the paper](https://arxiv.org/abs/2203.05437) |
tyzhu/squad_qa_wrong_num_v5_full_no_permute | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 7568134.963687525
num_examples: 4778
- name: validation
num_bytes: 346484
num_examples: 300
download_size: 1174396
dataset_size: 7914618.963687525
---
# Dataset Card for "squad_qa_wrong_num_v5_full_no_permute"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mitalimorker/questions-datasets | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 281
num_examples: 11
download_size: 1663
dataset_size: 281
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
neuclir/neuclir1 | ---
annotations_creators:
- no-annotation
language:
- fa
- ru
- zh
language_creators:
- found
license:
- odc-by
multilinguality:
- multilingual
pretty_name: NeuCLIR1
size_categories:
- 1M<n<10M
source_datasets:
- extended|c4
tags: []
task_categories:
- text-retrieval
task_ids:
- document-retrieval
---
# Dataset Card for NeuCLIR1
## Dataset Description
- **Website:** https://neuclir.github.io/
- **Repository:** https://github.com/NeuCLIR/download-collection
### Dataset Summary
This is the dataset created for TREC 2022 NeuCLIR Track. The collection designed to be similar to HC4 and a large portion of documents from HC4 are ported to this collection.
The documents are Web pages from Common Crawl in Chinese, Persian, and Russian.
### Languages
- Chinese
- Persian
- Russian
## Dataset Structure
### Data Instances
| Split | Documents |
|-----------------|----------:|
| `fas` (Persian) | 2.2M |
| `rus` (Russian) | 4.6M |
| `zho` (Chinese) | 3.2M |
### Data Fields
- `id`: unique identifier for this document
- `cc_file`: source file from connon crawl
- `time`: extracted date/time from article
- `title`: title extracted from article
- `text`: extracted article body
- `url`: source URL
## Dataset Usage
Using ๐ค Datasets:
```python
from datasets import load_dataset
dataset = load_dataset('neuclir/neuclir1')
dataset['fas'] # Persian documents
dataset['rus'] # Russian documents
dataset['zho'] # Chinese documents
```
|
CATIE-AQ/anli_fr_prompt_textual_entailment | ---
licence : cc-by-nc-4.0
language:
- fr
size_categories:
- 100K<n<1M
task_categories:
- text-classification
tags:
- textual-entailment
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- multilingual-NLI-26lang-2mil7
---
# anli_fr_prompt_textual_entailment
## Summary
**anli_fr_prompt_textual_entailment** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **550,000** rows that can be used for a textual entailment task.
The original data (without prompts) comes from the dataset [multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) by Laurer et al. where only the anli French part has been kept.
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
22 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
"""Prendre l'รฉnoncรฉ suivant comme vrai : " """+premise+""" "\n Alors l'รฉnoncรฉ suivant : " """+hypothesis+""" " est "vrai", "faux", ou "incertain" ?""",
"""Prends l'รฉnoncรฉ suivant comme vrai : " """+premise+""" "\n Alors l'รฉnoncรฉ suivant : " """+hypothesis+""" " est "vrai", "faux", ou "incertain" ?""",
"""Prenez l'รฉnoncรฉ suivant comme vrai : " """+premise+""" "\n Alors l'รฉnoncรฉ suivant : " """+hypothesis+""" " est "vrai", "faux", ou "incertain" ?""",
'"'+premise+'"\nQuestion : Cela implique-t-il que "'+hypothesis+'" ? "vrai", "faux", ou "incertain" ?',
'"'+premise+'"\nQuestion : "'+hypothesis+'" est "vrai", "faux", ou "peut-รชtre" ?',
""" " """+premise+""" "\n D'aprรจs le passage prรฉcรฉdent, est-il vrai que " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?""",
""" " """+premise+""" "\nSur la base de ces informations, l'รฉnoncรฉ est-il : " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?""",
""" " """+premise+""" "\nEn gardant ร l'esprit le texte ci-dessus, considรฉrez : " """+hypothesis+""" "\n Est-ce que c'est "vrai", "faux", ou "incertain" ?""",
""" " """+premise+""" "\nEn gardant ร l'esprit le texte ci-dessus, considรฉre : " """+hypothesis+""" "\n Est-ce que c'est "vrai", "faux", ou "peut-รชtre" ?""",
""" " """+premise+""" "\nEn utilisant uniquement la description ci-dessus et ce que vous savez du monde, " """+hypothesis+""" " est-ce "vrai", "faux", ou "incertain" ?""",
""" " """+premise+""" "\nEn utilisant uniquement la description ci-dessus et ce que tu sais du monde, " """+hypothesis+""" " est-ce "vrai", "faux", ou "incertain" ?""",
"""รtant donnรฉ que " """+premise+""" ", s'ensuit-il que " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?""",
"""รtant donnรฉ que " """+premise+""" ", est-il garanti que " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?""",
'รtant donnรฉ '+premise+', doit-on supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',
'รtant donnรฉ '+premise+', dois-je supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',
'Sachant que '+premise+', doit-on supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',
'Sachant que '+premise+', dois-je supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',
'รtant donnรฉ que '+premise+', il doit donc รชtre vrai que '+hypothesis+' ? "vrai", "faux", ou "incertain" ?',
"""Supposons que " """+premise+""" ", pouvons-nous dรฉduire que " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?""",
"""Supposons que " """+premise+""" ", puis-je dรฉduire que " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?""",
"""Supposons qu'il est vrai que " """+premise+""" ". Alors, est-ce que " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?""",
"""Supposons qu'il soit vrai que " """+premise+""" ",\n Donc, " """+hypothesis+""" " ? "vrai", "faux", ou "incertain" ?"""
```
### Features used in the prompts
In the prompt list above, `premise`, `hypothesis` and `targets` have been constructed from:
```
moritz = load_dataset('MoritzLaurer/multilingual-NLI-26lang-2mil7')
anli = moritz['fr_anli']
anli['premise'] = list(map(lambda i: i.replace(' . ','. ').replace(' .','. ').replace('( ','(').replace(' )',')').replace(' , ',', ').replace(', ',', ').replace("' ","'"), map(str,anli['premise'])))
anli['hypothesis'] = list(map(lambda x: x.replace(' . ','. ').replace(' .','. ').replace('( ','(').replace(' )',')').replace(' , ',', ').replace(', ',', ').replace("' ","'"), map(str,anli['hypothesis'])))
targets = str(anli['label'][i]).replace("0","vrai").replace("1","incertain").replace("2","faux")
```
# Splits
- `train` with 550,000 samples
- no `valid` split
- no `test` split
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/anli_fr_prompt_textual_entailment")
```
# Citation
## Original data
> @article{laurer_less_2022,
title = {Less {Annotating}, {More} {Classifying} โ {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}},
url = {https://osf.io/74b8k},
language = {en-us},
urldate = {2022-07-28},
journal = {Preprint},
author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper},
month = jun,
year = {2022},
note = {Publisher: Open Science Framework},
}
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
cc-by-nc-4.0 |
yuyijiong/LongAlpaca-Chinese | ---
license: cc-by-nc-4.0
language:
- zh
---
* ไปฟ็
ง[Yukang/LongAlpaca-12k](https://huggingface.co/datasets/Yukang/LongAlpaca-12k)่ฎพ่ฎก็ไธญๆ็LongAlpacaๆฐๆฎ้๏ผๆฐๆฎ่ดจ้ๆด้ซ๏ผ็ฑปๅๆดๅค๏ผ้ฟๅบฆๆด้ฟ๏ผไธไฝฟ็จๅค่ฝฎๅฏน่ฏๅฝขๅผใ
* ๅฏๆปก่ถณๆฉๅฑๆจกๅcontext window่ณ32k้ฟๅบฆ็ๆไปคๅพฎ่ฐ่ฎญ็ปใ
* ๆๆๆฐๆฎ็textๅๅทฒ็ป่ฝฌๅไธบchatmlๅฏน่ฏๆ ผๅผใ
## ๆฐๆฎ้ๆ ทๆฌ้ฟๅบฆ็ป่ฎก๏ผไฝฟ็จqwen็tokenizer่ฟ่กๅ่ฏ๏ผ๏ผ
input_ids้ฟๅบฆไธบ0-4096็ๆ ทๆฌๆฐ๏ผ1144๏ผๅ ๆฏ๏ผ0.12 \
input_ids้ฟๅบฆไธบ4096-8192็ๆ ทๆฌๆฐ๏ผ1103๏ผๅ ๆฏ๏ผ0.11 \
input_ids้ฟๅบฆไธบ8192-16384็ๆ ทๆฌๆฐ๏ผ2245๏ผๅ ๆฏ๏ผ0.24 \
input_ids้ฟๅบฆไธบ16384-24576็ๆ ทๆฌๆฐ๏ผ990๏ผๅ ๆฏ๏ผ0.10 \
input_ids้ฟๅบฆไธบ24576-32768็ๆ ทๆฌๆฐ๏ผ3661๏ผๅ ๆฏ๏ผ0.39 \
input_ids้ฟๅบฆๅคงไบ32768็ๆ ทๆฌๆฐ๏ผ196 \
ๆปๆ ทๆฌๆฐ๏ผ9339 \
ๅนณๅ้ฟๅบฆ๏ผ18292.95920334083 |
alexses2200/ARCHIVE | ---
license: other
---
|
bohdan1/knowd | ---
license: mit
---
|
anan-2024/twitter_dataset_1713199353 | ---
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: 123195
num_examples: 336
download_size: 67823
dataset_size: 123195
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pvduy/ultra-mix-ak-48k | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 214023291
num_examples: 48110
- name: test
num_bytes: 448467
num_examples: 100
download_size: 110583980
dataset_size: 214471758
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
tasksource/ConTRoL-nli | ---
task_categories:
- text-classification
language:
- en
---
https://github.com/csitfun/ConTRoL-dataset
```
@article{Liu_Cui_Liu_Zhang_2021,
title={Natural Language Inference in Context - Investigating Contextual Reasoning over Long Texts},
volume={35},
url={https://ojs.aaai.org/index.php/AAAI/article/view/17580},
DOI={10.1609/aaai.v35i15.17580},
number={15},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Liu, Hanmeng and Cui, Leyang and Liu, Jian and Zhang, Yue},
year={2021},
month={May},
pages={13388-13396}
}
``` |
open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged | ---
pretty_name: Evaluation run of dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged](https://huggingface.co/dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged)\
\ 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_dhmeltzer__llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T03:13:00.871936](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged/blob/main/results_2023-10-26T03-13-00.871936.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\": 0.001572986577181208,\n\
\ \"em_stderr\": 0.0004058451132417744,\n \"f1\": 0.06281459731543623,\n\
\ \"f1_stderr\": 0.0014401527427077175,\n \"acc\": 0.3901322603943458,\n\
\ \"acc_stderr\": 0.009101657407871456\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.0004058451132417744,\n\
\ \"f1\": 0.06281459731543623,\n \"f1_stderr\": 0.0014401527427077175\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04624715693707354,\n \
\ \"acc_stderr\": 0.005784991662691866\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.734017363851618,\n \"acc_stderr\": 0.012418323153051046\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged
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_31T20_11_58.218501
path:
- '**/details_harness|arc:challenge|25_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|arc:challenge|25_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T03_13_00.871936
path:
- '**/details_harness|drop|3_2023-10-26T03-13-00.871936.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T03-13-00.871936.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T03_13_00.871936
path:
- '**/details_harness|gsm8k|5_2023-10-26T03-13-00.871936.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T03-13-00.871936.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hellaswag|10_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hellaswag|10_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T20:11:58.218501.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T20:11:58.218501.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T11:01:05.889294.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T11:01:05.889294.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T11:01:05.889294.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-31T20:11:58.218501.parquet'
- split: 2023_09_05T11_01_05.889294
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-05T11:01:05.889294.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-05T11:01:05.889294.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T03_13_00.871936
path:
- '**/details_harness|winogrande|5_2023-10-26T03-13-00.871936.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T03-13-00.871936.parquet'
- config_name: results
data_files:
- split: 2023_08_31T20_11_58.218501
path:
- results_2023-08-31T20:11:58.218501.parquet
- split: 2023_09_05T11_01_05.889294
path:
- results_2023-09-05T11:01:05.889294.parquet
- split: 2023_10_26T03_13_00.871936
path:
- results_2023-10-26T03-13-00.871936.parquet
- split: latest
path:
- results_2023-10-26T03-13-00.871936.parquet
---
# Dataset Card for Evaluation run of dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged
- **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 [dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged](https://huggingface.co/dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged) 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_dhmeltzer__llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T03:13:00.871936](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged/blob/main/results_2023-10-26T03-13-00.871936.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": 0.001572986577181208,
"em_stderr": 0.0004058451132417744,
"f1": 0.06281459731543623,
"f1_stderr": 0.0014401527427077175,
"acc": 0.3901322603943458,
"acc_stderr": 0.009101657407871456
},
"harness|drop|3": {
"em": 0.001572986577181208,
"em_stderr": 0.0004058451132417744,
"f1": 0.06281459731543623,
"f1_stderr": 0.0014401527427077175
},
"harness|gsm8k|5": {
"acc": 0.04624715693707354,
"acc_stderr": 0.005784991662691866
},
"harness|winogrande|5": {
"acc": 0.734017363851618,
"acc_stderr": 0.012418323153051046
}
}
```
### 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] |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_72 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1324194968.0
num_examples: 260054
download_size: 1350780647
dataset_size: 1324194968.0
---
# Dataset Card for "chunk_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TinyPixel/tc-1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2306273602
num_examples: 1000000
download_size: 1044132769
dataset_size: 2306273602
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tc-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
automated-research-group/winogrande_inverted_option | ---
dataset_info:
features:
- name: id
dtype: string
- name: response
dtype: string
- name: request
dtype: string
splits:
- name: validation
num_bytes: 316710
num_examples: 1267
download_size: 123029
dataset_size: 316710
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "winogrande_inverted_option"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SEACrowd/indspeech_news_ethnicsr | ---
tags:
- speech-recognition
language:
- sun
- jav
---
# indspeech_news_ethnicsr
INDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Technology (ITB, Indonesia) [Sani et al., 2012].
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{sani-cocosda-2012,
title = "Towards Language Preservation: Preliminary Collection and Vowel Analysis of {I}ndonesian Ethnic Speech Data",
author = "Sani, Auliya and Sakti, Sakriani and Neubig, Graham and Toda, Tomoki and Mulyanto, Adi and Nakamura, Satoshi",
booktitle = "Proc. Oriental COCOSDA",
year = "2012",
pages = "118--122"
address = "Macau, China"
}
```
## License
CC-BY-NC-SA 4.0
## Homepage
[https://github.com/s-sakti/data_indsp_news_ethnicsr](https://github.com/s-sakti/data_indsp_news_ethnicsr)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
wessmetal/alex | ---
license: bsd
---
|
Lilithchouy/1111bb | ---
license: afl-3.0
---
11 |
Helsinki-NLP/europarl | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: europarl
pretty_name: OPUS Europarl (European Parliament Proceedings Parallel Corpus)
dataset_info:
- config_name: bg-cs
features:
- name: translation
dtype:
translation:
languages:
- bg
- cs
splits:
- name: train
num_bytes: 175371803
num_examples: 402657
download_size: 87804698
dataset_size: 175371803
- config_name: bg-da
features:
- name: translation
dtype:
translation:
languages:
- bg
- da
splits:
- name: train
num_bytes: 169901015
num_examples: 393449
download_size: 82104370
dataset_size: 169901015
- config_name: bg-de
features:
- name: translation
dtype:
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languages:
- bg
- de
splits:
- name: train
num_bytes: 179830375
num_examples: 393298
download_size: 87331979
dataset_size: 179830375
- config_name: bg-el
features:
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dtype:
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languages:
- bg
- el
splits:
- name: train
num_bytes: 232659595
num_examples: 377341
download_size: 105385882
dataset_size: 232659595
- config_name: bg-en
features:
- name: translation
dtype:
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languages:
- bg
- en
splits:
- name: train
num_bytes: 175001915
num_examples: 408290
download_size: 84692309
dataset_size: 175001915
- config_name: bg-es
features:
- name: translation
dtype:
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languages:
- bg
- es
splits:
- name: train
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num_examples: 388226
download_size: 84714028
dataset_size: 175607796
- config_name: bg-et
features:
- name: translation
dtype:
translation:
languages:
- bg
- et
splits:
- name: train
num_bytes: 169828009
num_examples: 400712
download_size: 83951621
dataset_size: 169828009
- config_name: bg-fi
features:
- name: translation
dtype:
translation:
languages:
- bg
- fi
splits:
- name: train
num_bytes: 173345606
num_examples: 396624
download_size: 84672747
dataset_size: 173345606
- config_name: bg-fr
features:
- name: translation
dtype:
translation:
languages:
- bg
- fr
splits:
- name: train
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download_size: 86585352
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- config_name: bg-hu
features:
- name: translation
dtype:
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languages:
- bg
- hu
splits:
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download_size: 85156382
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- config_name: bg-it
features:
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dtype:
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languages:
- bg
- it
splits:
- name: train
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download_size: 86687785
dataset_size: 178371723
- config_name: bg-lt
features:
- name: translation
dtype:
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languages:
- bg
- lt
splits:
- name: train
num_bytes: 168241858
num_examples: 392554
download_size: 83079417
dataset_size: 168241858
- config_name: bg-lv
features:
- name: translation
dtype:
translation:
languages:
- bg
- lv
splits:
- name: train
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download_size: 84782236
dataset_size: 173267354
- config_name: bg-nl
features:
- name: translation
dtype:
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languages:
- bg
- nl
splits:
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download_size: 84756271
dataset_size: 174737241
- config_name: bg-pl
features:
- name: translation
dtype:
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languages:
- bg
- pl
splits:
- name: train
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download_size: 87408496
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- config_name: bg-pt
features:
- name: translation
dtype:
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languages:
- bg
- pt
splits:
- name: train
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download_size: 84957242
dataset_size: 174578643
- config_name: bg-ro
features:
- name: translation
dtype:
translation:
languages:
- bg
- ro
splits:
- name: train
num_bytes: 175217952
num_examples: 389381
download_size: 85287374
dataset_size: 175217952
- config_name: bg-sk
features:
- name: translation
dtype:
translation:
languages:
- bg
- sk
splits:
- name: train
num_bytes: 170976907
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download_size: 85504510
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- config_name: bg-sl
features:
- name: translation
dtype:
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languages:
- bg
- sl
splits:
- name: train
num_bytes: 159371222
num_examples: 380231
download_size: 79481810
dataset_size: 159371222
- config_name: bg-sv
features:
- name: translation
dtype:
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languages:
- bg
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---
# Dataset Card for OPUS Europarl (European Parliament Proceedings Parallel Corpus)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://opus.nlpl.eu/Europarl/corpus/version/Europarl
- **Homepage:** https://www.statmt.org/europarl/
- **Repository:** [OPUS Europarl](https://opus.nlpl.eu/Europarl.php)
- **Paper:** https://aclanthology.org/2005.mtsummit-papers.11/
- **Paper:** https://aclanthology.org/L12-1246/
- **Leaderboard:** [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)
### Dataset Summary
A parallel corpus extracted from the European Parliament web site by Philipp Koehn (University of Edinburgh).
The main intended use is to aid statistical machine translation research.
More information can be found at http://www.statmt.org/europarl/
### Supported Tasks and Leaderboards
Tasks: Machine Translation, Cross Lingual Word Embeddings (CWLE) Alignment
### Languages
- 21 languages, 211 bitexts
- total number of files: 207,775
- total number of tokens: 759.05M
- total number of sentence fragments: 30.32M
Every pair of the following languages is available:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- nl
- pl
- pt
- ro
- sk
- sl
- sv
## Dataset Structure
### Data Instances
Here is an example from the en-fr pair:
```
{
'translation': {
'en': 'Resumption of the session',
'fr': 'Reprise de la session'
}
}
```
### Data Fields
- `translation`: a dictionary containing two strings paired with a key indicating the corresponding language.
### Data Splits
- `train`: only train split is provided. Authors did not provide a separation of examples in `train`, `dev` and `test`.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The data set comes with the same license
as the original sources.
Please, check the information about the source
that is given on
https://opus.nlpl.eu/Europarl/corpus/version/Europarl
The terms of use of the original source dataset are:
> We are not aware of any copyright restrictions of the material. If you use this data in your research, please contact phi@jhu.edu.
### Citation Information
Please cite the paper, if you use this corpus in your work:
```
@inproceedings{koehn-2005-europarl,
title = "{E}uroparl: A Parallel Corpus for Statistical Machine Translation",
author = "Koehn, Philipp",
booktitle = "Proceedings of Machine Translation Summit X: Papers",
month = sep # " 13-15",
year = "2005",
address = "Phuket, Thailand",
url = "https://aclanthology.org/2005.mtsummit-papers.11",
pages = "79--86",
}
```
Please cite the following article if you use any part of the corpus in your own work:
```
@inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
pages = "2214--2218",
}
```
### Contributions
Thanks to [@lucadiliello](https://github.com/lucadiliello) for adding this dataset. |
pythainlp/prd_news_30112023 | ---
language:
- th
license: cc0-1.0
size_categories:
- 100K<n<1M
task_categories:
- text-generation
dataset_info:
features:
- name: NewsTitle
dtype: string
- name: Detail
dtype: string
- name: NewsDate
dtype: string
- name: Region
dtype: string
- name: Province
dtype: string
- name: Department
dtype: string
- name: Link_News
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1191831633
num_examples: 242985
download_size: 389528580
dataset_size: 1191831633
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- news
---
# PRD News 30112023
Thai News from The Government Public Relations Department, Office of the Prime Minister. We are collect Thai News from Open Data.
Sources:
- เธเนเธฒเธงเธเธฅเธฑเธชเนเธเธญเธฃเนเธเธฅเธญเธเนเธเธข [https://gdcatalog.go.th/dataset/gdpublish-klongtoei](https://gdcatalog.go.th/dataset/gdpublish-klongtoei)
- เธเนเธฒเธงเธซเธกเธญเธเธฃเนเธญเธก [https://gdcatalog.go.th/dataset/gdpublish-morprom](https://gdcatalog.go.th/dataset/gdpublish-morprom)
- เธเนเธฒเธงPM 2.5 [https://gdcatalog.go.th/dataset/gdpublish-pm-2-5](https://gdcatalog.go.th/dataset/gdpublish-pm-2-5)
- เธเนเธฒเธงเนเธเธทเนเธญเนเธเธฉเธเธฃเธเธฃ [https://gdcatalog.go.th/dataset/gdpublish-kaset0564](https://gdcatalog.go.th/dataset/gdpublish-kaset0564)
- เธเนเธฒเธงเธชเธฒเธฃเนเธเธงเธดเธ 19 (Covid -19) [https://gdcatalog.go.th/dataset/gdpublish-covid_may2564](https://gdcatalog.go.th/dataset/gdpublish-covid_may2564)
- เธเนเธฒเธงเธเธฃเธฐเธเธฑเธเธชเธฑเธเธเธก [https://gdcatalog.go.th/dataset/gdpublish-socialmay64](https://gdcatalog.go.th/dataset/gdpublish-socialmay64)
- เธเนเธฒเธงเธเนเธญเธเนเธเธตเนเธขเธงเนเธเธข [https://gdcatalog.go.th/dataset/gdpublish-travel_may64](https://gdcatalog.go.th/dataset/gdpublish-travel_may64)
- เธเนเธฒเธงเธงเธฑเธเธเธตเธpfizer [https://gdcatalog.go.th/dataset/gdpublish-pfizer](https://gdcatalog.go.th/dataset/gdpublish-pfizer)
- เธเนเธฒเธงเธฃเธฑเธเธชเธกเธฑเธเธฃเธเธฒเธเธฃเธฒเธเธเธฒเธฃ [https://gdcatalog.go.th/dataset/gdpublish-govjob_may64](https://gdcatalog.go.th/dataset/gdpublish-govjob_may64)
- เธเนเธฒเธงเธงเธฑเธเธเธตเธ sinovac [https://gdcatalog.go.th/dataset/gdpublish-sinovac](https://gdcatalog.go.th/dataset/gdpublish-sinovac)
Source (still update)
- เธเนเธฒเธงเธฃเธฒเธขเธเธฒเธเธเธดเนเธจเธฉเนเธฅเธฐเธเธเธเธงเธฒเธก [https://gdcatalog.go.th/dataset/gdpublish-special052021](https://gdcatalog.go.th/dataset/gdpublish-special052021)
- เธเนเธฒเธงเธเนเธฒเธเธเธฃเธฐเนเธเธจ [https://gdcatalog.go.th/dataset/gdpublish-wordnews052021](https://gdcatalog.go.th/dataset/gdpublish-wordnews052021)
- เธเนเธฒเธงเธชเธฒเธเธฒเธฃเธเธ เธฑเธข [https://gdcatalog.go.th/dataset/gdpublish-dataset-22_101](https://gdcatalog.go.th/dataset/gdpublish-dataset-22_101)
- เธเนเธฒเธงเธเธฒเธฃเนเธกเธทเธญเธ [https://gdcatalog.go.th/dataset/gdpublish-politicsnews052021](https://gdcatalog.go.th/dataset/gdpublish-politicsnews052021)
- เธเนเธฒเธงเนเธเธฉเธเธฃ/เธชเธดเนเธเนเธงเธเธฅเนเธญเธก [https://gdcatalog.go.th/dataset/gdpublish-641](https://gdcatalog.go.th/dataset/gdpublish-641)
- เธเนเธฒเธงเธเธตเธฌเธฒ [https://gdcatalog.go.th/dataset/gdpublish-sportnews052021](https://gdcatalog.go.th/dataset/gdpublish-sportnews052021)
- เธเนเธฒเธงเธชเธฑเธเธเธก [https://gdcatalog.go.th/dataset/gdpublish-aroundthainews052021](https://gdcatalog.go.th/dataset/gdpublish-aroundthainews052021)
- เธเนเธฒเธงเธงเธดเธเธขเน เนเธเธเนเธ [https://gdcatalog.go.th/dataset/gdpublish-dataset-22_11](https://gdcatalog.go.th/dataset/gdpublish-dataset-22_11)
- เธเนเธฒเธงเธจเธดเธฅเธเธฐ เธงเธฑเธเธเธเธฃเธฃเธก [https://gdcatalog.go.th/dataset/gdpublish-art052021](https://gdcatalog.go.th/dataset/gdpublish-art052021)
- เธเนเธฒเธงเนเธจเธฃเธฉเธเธเธดเธ เธเนเธญเธเนเธเธตเนเธขเธง [https://gdcatalog.go.th/dataset/gdpublish-economynews052021](https://gdcatalog.go.th/dataset/gdpublish-economynews052021)
- เธเนเธฒเธงเธเธฃเธฐเธเธฒเธชเธฑเธกเธเธฑเธเธเน [https://gdcatalog.go.th/dataset/gdpublish-dataset-22_12](https://gdcatalog.go.th/dataset/gdpublish-dataset-22_12)
- เธเนเธฒเธงเนเธเธเธฃเธฐเธฃเธฒเธเธชเธณเธเธฑเธ [https://gdcatalog.go.th/dataset/gdpublish-dataset-22_071](https://gdcatalog.go.th/dataset/gdpublish-dataset-22_071)
license: cc0-1.0 |
irds/clueweb09_ja | ---
pretty_name: '`clueweb09/ja`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `clueweb09/ja`
The `clueweb09/ja` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb09#clueweb09/ja).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=67,337,717
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/clueweb09_ja', 'docs')
for record in docs:
record # {'doc_id': ..., 'url': ..., 'date': ..., 'http_headers': ..., 'body': ..., 'body_content_type': ...}
```
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 ๐ค Dataset format.
|
TheFinAI/NWGI_test | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 6885237
num_examples: 12947
- name: valid
num_bytes: 1713486
num_examples: 3237
- name: test
num_bytes: 2154392
num_examples: 4047
download_size: 2852286
dataset_size: 10753115
license: mit
language:
- en
---
# Dataset Card for "NWGI_test"
## Dataset Class:
``` python
class NWGI(InstructionDataset):
dataset = "NWGI"
task_type = "classification"
choices = [
'strong negative', 'moderately negative', 'mildly negative', 'neutral',
'mildly positive', 'moderately positive', 'strong positive',
]
prompt = '''What is the sentiment of this news?
{input}
Please choose an answer from {{strong negative/moderately negative/mildly negative/neutral/mildly positive/moderately positive/strong positive}}.'''
def fetch_data(self, datum):
return {'input': datum['input'], 'answer': datum['answer']}
``` |
tyzhu/find_first_sent_train_100_eval_10_baseline | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 169972
num_examples: 100
- name: validation
num_bytes: 17771
num_examples: 10
download_size: 0
dataset_size: 187743
---
# Dataset Card for "find_first_sent_train_100_eval_10_baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Alex7756/gf_0909 | ---
license: other
---
|
vidhikatkoria/DA_SGD_Homes | ---
dataset_info:
features:
- name: domain
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: act
dtype: int64
- name: speaker
dtype: int64
splits:
- name: train
num_bytes: 782436.7041420118
num_examples: 3041
- name: test
num_bytes: 418
num_examples: 1
download_size: 311067
dataset_size: 782854.7041420118
---
# Dataset Card for "DA_SGD_Homes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ciempiess/tedx_spanish | ---
license: cc-by-nc-nd-4.0
---
|
CyberHarem/pudding_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of pudding/ใใชใณ/ๅธไธ (Arknights)
This is the dataset of pudding/ใใชใณ/ๅธไธ (Arknights), containing 56 images and their tags.
The core tags of this character are `pink_hair, hairband, black_hairband, breasts, animal_ears, cat_ears, yellow_eyes, cat_girl, long_hair, 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 | 56 | 79.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pudding_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 56 | 66.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pudding_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 135 | 127.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pudding_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/pudding_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 | 18 |  |  |  |  |  | 1girl, blush, lightning_bolt_print, cat_tail, large_breasts, open_mouth, simple_background, single_braid, floppy_ears, hair_bow, heart, black_skirt, collar, long_sleeves, nipples, open_jacket, white_background, white_shirt, blue_bow, infection_monitor_(arknights), pink_jacket, solo_focus, white_thighhighs |
| 1 | 9 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, open_jacket, solo, black_gloves, fingerless_gloves, thigh_strap, black_footwear, blue_jacket, full_body, shoes, white_socks, holding_staff, simple_background, stuffed_animal, white_background, white_dress, closed_mouth, pink_eyes |
| 2 | 5 |  |  |  |  |  | 1girl, open_jacket, solo, turtleneck_sweater, white_sweater, black_gloves, blue_background, fingerless_gloves, long_sleeves, stuffed_animal, thick_eyebrows, upper_body, infection_monitor_(arknights), looking_at_viewer, blue_jacket, blush, brown_eyes, closed_mouth, holding_stuffed_toy, id_card, low_twintails, seal_(animal), short_twintails, single_glove, whisker_markings, white_dress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | lightning_bolt_print | cat_tail | large_breasts | open_mouth | simple_background | single_braid | floppy_ears | hair_bow | heart | black_skirt | collar | long_sleeves | nipples | open_jacket | white_background | white_shirt | blue_bow | infection_monitor_(arknights) | pink_jacket | solo_focus | white_thighhighs | looking_at_viewer | solo | black_gloves | fingerless_gloves | thigh_strap | black_footwear | blue_jacket | full_body | shoes | white_socks | holding_staff | stuffed_animal | white_dress | closed_mouth | pink_eyes | turtleneck_sweater | white_sweater | blue_background | thick_eyebrows | upper_body | brown_eyes | holding_stuffed_toy | id_card | low_twintails | seal_(animal) | short_twintails | single_glove | whisker_markings |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------------------|:-----------|:----------------|:-------------|:--------------------|:---------------|:--------------|:-----------|:--------|:--------------|:---------|:---------------|:----------|:--------------|:-------------------|:--------------|:-----------|:--------------------------------|:--------------|:-------------|:-------------------|:--------------------|:-------|:---------------|:--------------------|:--------------|:-----------------|:--------------|:------------|:--------|:--------------|:----------------|:-----------------|:--------------|:---------------|:------------|:---------------------|:----------------|:------------------|:-----------------|:-------------|:-------------|:----------------------|:----------|:----------------|:----------------|:------------------|:---------------|:-------------------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | | | | | | X | | | | | | | X | | X | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | 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 |
|
hieunguyenminh/baseTextData | ---
task_categories:
- conversational
- text-classification
- question-answering
- translation
- text2text-generation
- summarization
size_categories:
- 10K<n<100K
--- |
distilled-from-one-sec-cv12/chunk_50 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1161161188
num_examples: 226259
download_size: 1178057940
dataset_size: 1161161188
---
# Dataset Card for "chunk_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_dvruette__gpt-neox-20b-full-precision | ---
pretty_name: Evaluation run of dvruette/gpt-neox-20b-full-precision
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dvruette/gpt-neox-20b-full-precision](https://huggingface.co/dvruette/gpt-neox-20b-full-precision)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dvruette__gpt-neox-20b-full-precision\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T10:49:53.793437](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__gpt-neox-20b-full-precision/blob/main/results_2023-10-15T10-49-53.793437.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\": 0.008179530201342282,\n\
\ \"em_stderr\": 0.0009224022743034369,\n \"f1\": 0.06148594798657739,\n\
\ \"f1_stderr\": 0.0015819609377213373,\n \"acc\": 0.35462516448027825,\n\
\ \"acc_stderr\": 0.008753822199298638\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.008179530201342282,\n \"em_stderr\": 0.0009224022743034369,\n\
\ \"f1\": 0.06148594798657739,\n \"f1_stderr\": 0.0015819609377213373\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.026535253980288095,\n \
\ \"acc_stderr\": 0.0044270459872651595\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6827150749802684,\n \"acc_stderr\": 0.013080598411332115\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dvruette/gpt-neox-20b-full-precision
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|arc:challenge|25_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T10_49_53.793437
path:
- '**/details_harness|drop|3_2023-10-15T10-49-53.793437.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T10-49-53.793437.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T10_49_53.793437
path:
- '**/details_harness|gsm8k|5_2023-10-15T10-49-53.793437.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T10-49-53.793437.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hellaswag|10_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T21:36:17.720122.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T21:36:17.720122.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T21:36:17.720122.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T10_49_53.793437
path:
- '**/details_harness|winogrande|5_2023-10-15T10-49-53.793437.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T10-49-53.793437.parquet'
- config_name: results
data_files:
- split: 2023_07_19T21_36_17.720122
path:
- results_2023-07-19T21:36:17.720122.parquet
- split: 2023_10_15T10_49_53.793437
path:
- results_2023-10-15T10-49-53.793437.parquet
- split: latest
path:
- results_2023-10-15T10-49-53.793437.parquet
---
# Dataset Card for Evaluation run of dvruette/gpt-neox-20b-full-precision
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dvruette/gpt-neox-20b-full-precision
- **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 [dvruette/gpt-neox-20b-full-precision](https://huggingface.co/dvruette/gpt-neox-20b-full-precision) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dvruette__gpt-neox-20b-full-precision",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T10:49:53.793437](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__gpt-neox-20b-full-precision/blob/main/results_2023-10-15T10-49-53.793437.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": 0.008179530201342282,
"em_stderr": 0.0009224022743034369,
"f1": 0.06148594798657739,
"f1_stderr": 0.0015819609377213373,
"acc": 0.35462516448027825,
"acc_stderr": 0.008753822199298638
},
"harness|drop|3": {
"em": 0.008179530201342282,
"em_stderr": 0.0009224022743034369,
"f1": 0.06148594798657739,
"f1_stderr": 0.0015819609377213373
},
"harness|gsm8k|5": {
"acc": 0.026535253980288095,
"acc_stderr": 0.0044270459872651595
},
"harness|winogrande|5": {
"acc": 0.6827150749802684,
"acc_stderr": 0.013080598411332115
}
}
```
### 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] |
autoevaluate/autoeval-eval-futin__feed-sen_en_-7dbe88-2245971658 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/feed
eval_info:
task: text_zero_shot_classification
model: facebook/opt-2.7b
metrics: []
dataset_name: futin/feed
dataset_config: sen_en_
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-2.7b
* Dataset: futin/feed
* Config: sen_en_
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
DanielSongShen/CLIP-food101-image-dataset-large_latents_hidden_states | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': apple_pie
'1': baby_back_ribs
'2': baklava
'3': beef_carpaccio
'4': beef_tartare
'5': beet_salad
'6': beignets
'7': bibimbap
'8': bread_pudding
'9': breakfast_burrito
'10': bruschetta
'11': caesar_salad
'12': cannoli
'13': caprese_salad
'14': carrot_cake
'15': ceviche
'16': cheesecake
'17': cheese_plate
'18': chicken_curry
'19': chicken_quesadilla
'20': chicken_wings
'21': chocolate_cake
'22': chocolate_mousse
'23': churros
'24': clam_chowder
'25': club_sandwich
'26': crab_cakes
'27': creme_brulee
'28': croque_madame
'29': cup_cakes
'30': deviled_eggs
'31': donuts
'32': dumplings
'33': edamame
'34': eggs_benedict
'35': escargots
'36': falafel
'37': filet_mignon
'38': fish_and_chips
'39': foie_gras
'40': french_fries
'41': french_onion_soup
'42': french_toast
'43': fried_calamari
'44': fried_rice
'45': frozen_yogurt
'46': garlic_bread
'47': gnocchi
'48': greek_salad
'49': grilled_cheese_sandwich
'50': grilled_salmon
'51': guacamole
'52': gyoza
'53': hamburger
'54': hot_and_sour_soup
'55': hot_dog
'56': huevos_rancheros
'57': hummus
'58': ice_cream
'59': lasagna
'60': lobster_bisque
'61': lobster_roll_sandwich
'62': macaroni_and_cheese
'63': macarons
'64': miso_soup
'65': mussels
'66': nachos
'67': omelette
'68': onion_rings
'69': oysters
'70': pad_thai
'71': paella
'72': pancakes
'73': panna_cotta
'74': peking_duck
'75': pho
'76': pizza
'77': pork_chop
'78': poutine
'79': prime_rib
'80': pulled_pork_sandwich
'81': ramen
'82': ravioli
'83': red_velvet_cake
'84': risotto
'85': samosa
'86': sashimi
'87': scallops
'88': seaweed_salad
'89': shrimp_and_grits
'90': spaghetti_bolognese
'91': spaghetti_carbonara
'92': spring_rolls
'93': steak
'94': strawberry_shortcake
'95': sushi
'96': tacos
'97': takoyaki
'98': tiramisu
'99': tuna_tartare
'100': waffles
- name: CLIP_image_latent
sequence:
sequence: float32
- name: CLIP_hidden_states
sequence:
sequence: float32
splits:
- name: train
num_bytes: 102879094859.25
num_examples: 75750
- name: validation
num_bytes: 34292450392.75
num_examples: 25250
download_size: 138053316459
dataset_size: 137171545252.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
tiuratao/ronemar | ---
license: openrail
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/8fb7d2ec | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 188
num_examples: 10
download_size: 1335
dataset_size: 188
---
# Dataset Card for "8fb7d2ec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/shinano_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shinano/ไฟกๆฟ/ไฟกๆต (Azur Lane)
This is the dataset of shinano/ไฟกๆฟ/ไฟกๆต (Azur Lane), containing 500 images and their tags.
The core tags of this character are `animal_ears, long_hair, breasts, fox_ears, animal_ear_fluff, large_breasts, tail, fox_girl, fox_tail, bangs, blue_eyes, white_hair, multiple_tails, very_long_hair, grey_hair, hair_ornament, purple_eyes, white_tail`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shinano_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 483.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shinano_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1359 | 1.08 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shinano_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 876.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shinano_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1359 | 1.67 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shinano_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/shinano_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 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, nipples, solo, completely_nude, navel, blush, pussy, thighs, collarbone, censored, simple_background, stomach, white_background |
| 1 | 7 |  |  |  |  |  | 1girl, blush, cleavage, frilled_bikini, looking_at_viewer, solo, thighs, water, white_bikini, day, navel, outdoors, wet, bare_shoulders, parted_lips, stomach, blue_sky, sitting, collarbone |
| 2 | 7 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, navel, solo, stomach, white_bikini, cleavage, frills, thighs, blush, simple_background, detached_sleeves, hand_on_own_chest, sitting, water, white_background |
| 3 | 6 |  |  |  |  |  | 1girl, bare_shoulders, blush, cleavage, covered_navel, looking_at_viewer, official_alternate_costume, race_queen, solo, black_skirt, black_thighhighs, leotard, thighs, collarbone, microskirt, gloves, huge_breasts |
| 4 | 8 |  |  |  |  |  | 1girl, ass, bare_shoulders, looking_at_viewer, official_alternate_costume, race_queen, solo, from_behind, looking_back, thighs, elbow_gloves, black_thighhighs, kitsune, white_panties, blush, blue_skirt, microskirt, thigh_boots |
| 5 | 33 |  |  |  |  |  | 1girl, cleavage, off_shoulder, solo, white_thighhighs, wide_sleeves, bare_shoulders, blue_kimono, white_skirt, pleated_skirt, blue_collar, blue_butterfly, zettai_ryouiki, looking_at_viewer, kyuubi, long_sleeves, collarbone, huge_breasts, large_tail |
| 6 | 58 |  |  |  |  |  | cleavage, blue_dress, 1girl, bare_shoulders, looking_at_viewer, solo, official_alternate_costume, feather_boa, sleeveless_dress, blue_butterfly, halter_dress, blue_collar, blush, kyuubi, thighs |
| 7 | 5 |  |  |  |  |  | 1girl, blush, erection, huge_breasts, huge_penis, looking_at_viewer, nipples, outdoors, testicles, uncensored, veiny_penis, large_penis, blue_sky, blunt_bangs, cloud, day, navel, thick_thighs, 1boy, abs, armpits, arms_behind_head, arms_up, floral_print, futa_with_male, girl_on_top, kimono, muscular, sex, shiny_skin, solo_focus, spread_legs, squatting, straddling |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | nipples | solo | completely_nude | navel | blush | pussy | thighs | collarbone | censored | simple_background | stomach | white_background | cleavage | frilled_bikini | water | white_bikini | day | outdoors | wet | bare_shoulders | parted_lips | blue_sky | sitting | frills | detached_sleeves | hand_on_own_chest | covered_navel | official_alternate_costume | race_queen | black_skirt | black_thighhighs | leotard | microskirt | gloves | huge_breasts | ass | from_behind | looking_back | elbow_gloves | kitsune | white_panties | blue_skirt | thigh_boots | off_shoulder | white_thighhighs | wide_sleeves | blue_kimono | white_skirt | pleated_skirt | blue_collar | blue_butterfly | zettai_ryouiki | kyuubi | long_sleeves | large_tail | blue_dress | feather_boa | sleeveless_dress | halter_dress | erection | huge_penis | testicles | uncensored | veiny_penis | large_penis | blunt_bangs | cloud | thick_thighs | 1boy | abs | armpits | arms_behind_head | arms_up | floral_print | futa_with_male | girl_on_top | kimono | muscular | sex | shiny_skin | solo_focus | spread_legs | squatting | straddling |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:----------|:-------|:------------------|:--------|:--------|:--------|:---------|:-------------|:-----------|:--------------------|:----------|:-------------------|:-----------|:-----------------|:--------|:---------------|:------|:-----------|:------|:-----------------|:--------------|:-----------|:----------|:---------|:-------------------|:--------------------|:----------------|:-----------------------------|:-------------|:--------------|:-------------------|:----------|:-------------|:---------|:---------------|:------|:--------------|:---------------|:---------------|:----------|:----------------|:-------------|:--------------|:---------------|:-------------------|:---------------|:--------------|:--------------|:----------------|:--------------|:-----------------|:-----------------|:---------|:---------------|:-------------|:-------------|:--------------|:-------------------|:---------------|:-----------|:-------------|:------------|:-------------|:--------------|:--------------|:--------------|:--------|:---------------|:-------|:------|:----------|:-------------------|:----------|:---------------|:-----------------|:--------------|:---------|:-----------|:------|:-------------|:-------------|:--------------|:------------|:-------------|
| 0 | 7 |  |  |  |  |  | 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 | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | | X | | X | X | | X | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | | X | | | X | | X | | | | | | | | | | | | | X | | | | | | | | X | X | | X | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 33 |  |  |  |  |  | X | X | | X | | | | | | X | | | | | X | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 58 |  |  |  |  |  | X | X | | X | | | X | | X | | | | | | X | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | 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 | X |
|
jacksoncsie/Famous-Keyword-Twitter-Replies | ---
license: mit
---
The **"Famous Keyword Twitter Replies Dataset"** is a comprehensive collection of Twitter data that focuses on popular keywords and their associated replies. This dataset contains five essential columns that provide valuable insights into the Twitter conversation dynamics:
1. **Keyword:** This column represents the specific keyword or topic of interest that generated the original tweet. It helps identify the context or subject matter around which the conversation revolves.
2. **Main_tweet:** The main_tweet column contains the original tweet related to the keyword. It serves as the starting point or focal point of the conversation and often provides essential information or opinions on the given topic.
3. **Main_likes:** This column provides the number of likes received by the main_tweet. Likes serve as a measure of engagement and indicate the level of popularity or resonance of the original tweet within the Twitter community.
4. **Reply:** The reply column consists of the replies or responses to the main_tweet. These replies may include comments, opinions, additional information, or discussions related to the keyword or the original tweet itself. The replies help capture the diverse perspectives and conversations that emerge in response to the main_tweet.
5. **Reply_likes:** This column records the number of likes received by each reply. Similar to the main_likes column, the reply_likes column measures the level of engagement and popularity of individual replies. It enables the identification of particularly noteworthy or well-received replies within the dataset.
By analyzing this "Famous Keyword Twitter Replies Dataset," researchers, analysts, and data scientists can gain valuable insights into how popular keywords spark discussions on Twitter and how these discussions evolve through replies.
The dataset's information on likes allows for the evaluation of tweet and reply popularity, helping to identify influential or impactful content.
This dataset serves as a valuable resource for various applications, including sentiment analysis, trend identification, opinion mining, and understanding social media dynamics.
> Number of tweets for each pairs of tweet and reply
**Total has 17255 pairs of tweet/reply**
 |
ell-hol/ConceptualCaptionFR | ---
license: apache-2.0
---
|
DGurgurov/quechua_conceptnet | ---
license: mit
---
## ConceptNet Data for the Quechua Language
**Dataset Description:**
This dataset contains data extracted from ConceptNet using the dedicated module for fetching knowledge from the graph, available on [GitHub](https://github.com/d-gurgurov/Conceptnet-Embeddings).
**Data Structure:**
The data is converted from triplets into natural text using a pre-defined relationship mapping and split into training and validation sets. It was used for training language adapters for the project aimed at [injecting external commonsense knowledge into multilingual Large Language Models](https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs).
|
Bingsu/laion2B-multi-korean-subset | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ko
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: laion2B-multi-korean-subset
size_categories:
- 10M<n<100M
task_categories:
- feature-extraction
---
# laion2B-multi-korean-subset
## Dataset Description
- **Homepage:** [laion-5b](https://laion.ai/blog/laion-5b/)
- **Huggingface:** [laion/laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi)
## About dataset
a subset data of [laion/laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi), including only korean
### Lisence
CC-BY-4.0
## Data Structure
### Data Instance
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/laion2B-multi-korean-subset")
>>> dataset
DatasetDict({
train: Dataset({
features: ['SAMPLE_ID', 'URL', 'TEXT', 'HEIGHT', 'WIDTH', 'LICENSE', 'LANGUAGE', 'NSFW', 'similarity'],
num_rows: 11376263
})
})
```
```py
>>> dataset["train"].features
{'SAMPLE_ID': Value(dtype='int64', id=None),
'URL': Value(dtype='string', id=None),
'TEXT': Value(dtype='string', id=None),
'HEIGHT': Value(dtype='int32', id=None),
'WIDTH': Value(dtype='int32', id=None),
'LICENSE': Value(dtype='string', id=None),
'LANGUAGE': Value(dtype='string', id=None),
'NSFW': Value(dtype='string', id=None),
'similarity': Value(dtype='float32', id=None)}
```
### Data Size
download: 1.56 GiB<br>
generated: 2.37 GiB<br>
total: 3.93 GiB
### Data Field
- 'SAMPLE_ID': `int`
- 'URL': `string`
- 'TEXT': `string`
- 'HEIGHT': `int`
- 'WIDTH': `int`
- 'LICENSE': `string`
- 'LANGUAGE': `string`
- 'NSFW': `string`
- 'similarity': `float`
### Data Splits
| | train |
| --------- | -------- |
| # of data | 11376263 |
## Note
### Height, Width
์ด๋ฏธ์ง์ ๊ฐ๋ก๊ฐ `HEIGHT`๋ก, ์ธ๋ก๊ฐ `WIDTH`๋ก ๋์ด์๋ ๊ฒ ๊ฐ์ต๋๋ค.
```pycon
>>> dataset["train"][98]
{'SAMPLE_ID': 2937471001780,
'URL': 'https://image.ajunews.com/content/image/2019/04/12/20190412175643597949.png',
'TEXT': '์ธ์ฒ์๊ต์ก์ฒญ, ์ธ์ฒ ์๊ตฐ๊ตฌ๋ฐ์ ํ์ํ ์์์ง๊ณผ์ ๊ฐ๋ดํ ๊ฐ์ต',
'HEIGHT': 640,
'WIDTH': 321,
'LICENSE': '?',
'LANGUAGE': 'ko',
'NSFW': 'UNLIKELY',
'similarity': 0.33347243070602417}
```
### csv file, pandas
```py
# pip install zstandard
import pandas as pd
from huggingface_hub import hf_hub_url
url = hf_hub_url("Bingsu/laion2B-multi-korean-subset", filename="laion2B-multi-korean-subset.csv.zst", repo_type="dataset")
# url = "https://huggingface.co/datasets/Bingsu/laion2B-multi-korean-subset/resolve/main/laion2B-multi-korean-subset.csv.zst"
df = pd.read_csv(url)
```
<https://huggingface.co/datasets/Bingsu/laion2B-multi-korean-subset/resolve/main/laion2B-multi-korean-subset.csv.zst>
778 MB
### Code used to generate
```py
import csv
import re
from datasets import load_dataset
from tqdm import tqdm
pattern = re.compile(r"[๊ฐ-ํฃ]")
def quote(s: str) -> str:
s = s.replace('"""', "")
return s
def filter_func(example) -> bool:
lang = example.get("LANGUAGE")
text = example.get("TEXT")
if not isinstance(lang, str) or not isinstance(text, str):
return False
return lang == "ko" or pattern.search(text) is not None
file = open("./laion2B-mulit_korean_subset.csv", "w", encoding="utf-8", newline="")
ds = load_dataset("laion/laion2B-multi", split="train", streaming=True)
dsf = ds.filter(filter_func)
header = [
"SAMPLE_ID",
"URL",
"TEXT",
"HEIGHT",
"WIDTH",
"LICENSE",
"LANGUAGE",
"NSFW",
"similarity",
]
writer = csv.DictWriter(file, fieldnames=header)
writer.writeheader()
try:
for data in tqdm(dsf): # total=11378843
data["TEXT"] = quote(data.get("TEXT", ""))
if data["TEXT"]:
writer.writerow(data)
finally:
file.close()
print("Done!")
```
์คํ์ ์ฝ 8์๊ฐ์ด ์์๋์์ต๋๋ค. ์ดํ์ `HEIGHT`๋ `WIDTH`๊ฐ None์ธ ๋ฐ์ดํฐ๋ฅผ ์ ๊ฑฐํ๊ณ ์
๋ก๋ํ์์ต๋๋ค.
### img2dataset
[img2dataset](https://github.com/rom1504/img2dataset)์ ์ฌ์ฉํ์ฌ URL๋ก๋ ์ด๋ฏธ์ง๋ค์ ๋ฐ์ดํฐ์
ํํ๋ก ๋ง๋ค ์ ์์ต๋๋ค.
|
Spico/TaskLAMA | ---
license: cc-by-4.0
---
# TaskLAMA
This is an unofficial upload of the [TaskLAMA](https://arxiv.org/abs/2308.15299) data.
TaskLAMA is a novel dataset for Structured Complex Task Decomposition (SCTD).
Some of the data statistics could be found at [Spico197/TaskLAMA](https://github.com/Spico197/TaskLAMA) .
## Citation
```bibtex
@misc{yuan2023tasklama,
title={TaskLAMA: Probing the Complex Task Understanding of Language Models},
author={Quan Yuan and Mehran Kazemi and Xin Xu and Isaac Noble and Vaiva Imbrasaite and Deepak Ramachandran},
year={2023},
eprint={2308.15299},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
succinctly/midjourney-prompts | ---
license: apache-2.0
---
[Midjourney](https://midjourney.com) is an independent research lab whose broad mission is to "explore new mediums of thought". In 2022, they launched a text-to-image service that, given a natural language prompt, produces visual depictions that are faithful to the description. Their service is accessible via a public [Discord server](https://discord.com/invite/midjourney): users issue a query in natural language, and the Midjourney bot returns AI-generated images that follow the given description. The raw dataset (with Discord messages) can be found on Kaggle: [Midjourney User Prompts & Generated Images (250k)](https://www.kaggle.com/datasets/succinctlyai/midjourney-texttoimage). The authors of the scraped dataset have no affiliation to Midjourney.
This HuggingFace dataset was [processed](https://www.kaggle.com/code/succinctlyai/midjourney-text-prompts-huggingface) from the raw Discord messages to solely include the text prompts issued by the user (thus excluding the generated images and any other metadata). It could be used, for instance, to fine-tune a large language model to produce or auto-complete creative prompts for image generation.
Check out [succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator), a GPT-2 model fine-tuned on this dataset. |
sonueditor/adultsexchat | ---
license: apache-2.0
---
|
HuggingFaceFW/fineweb_german_extract | ---
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: media
sequence: 'null'
- name: metadata
struct:
- name: date
dtype: string
- name: dump
dtype: string
- name: file_path
dtype: string
- name: language
dtype: string
- name: language_score
dtype: float64
- name: url
dtype: string
splits:
- name: train
num_bytes: 28265580128
num_examples: 10000000
download_size: 14987610373
dataset_size: 28265580128
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/shinohara_rei_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shinohara_rei/็ฏ ๅ็คผ (THE iDOLM@STER: Cinderella Girls)
This is the dataset of shinohara_rei/็ฏ ๅ็คผ (THE iDOLM@STER: Cinderella Girls), containing 26 images and their tags.
The core tags of this character are `brown_hair, green_eyes, short_hair, breasts, earrings, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 26 | 17.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shinohara_rei_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 26 | 14.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shinohara_rei_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 47 | 24.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shinohara_rei_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 26 | 17.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shinohara_rei_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 47 | 29.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shinohara_rei_idolmastercinderellagirls/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/shinohara_rei_idolmastercinderellagirls',
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, solo, cleavage, necklace, smile, bare_shoulders, blush, collarbone, dress, looking_at_viewer, arm_support, simple_background, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | necklace | smile | bare_shoulders | blush | collarbone | dress | looking_at_viewer | arm_support | simple_background | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:-----------|:--------|:-----------------|:--------|:-------------|:--------|:--------------------|:--------------|:--------------------|:----------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
bebechien/shinyunbok_512 | ---
license: creativeml-openrail-m
---
|
liuyanchen1015/MULTI_VALUE_qqp_non_coordinated_obj_subj | ---
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: 626180
num_examples: 3289
- name: test
num_bytes: 6106978
num_examples: 31990
- name: train
num_bytes: 5800083
num_examples: 30191
download_size: 7870233
dataset_size: 12533241
---
# Dataset Card for "MULTI_VALUE_qqp_non_coordinated_obj_subj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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