datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
zoohun/low_test2 | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 16466
num_examples: 69
download_size: 8498
dataset_size: 16466
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_jisukim8873__falcon-7B-case-2 | ---
pretty_name: Evaluation run of jisukim8873/falcon-7B-case-2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jisukim8873/falcon-7B-case-2](https://huggingface.co/jisukim8873/falcon-7B-case-2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jisukim8873__falcon-7B-case-2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-04T05:03:52.331388](https://huggingface.co/datasets/open-llm-leaderboard/details_jisukim8873__falcon-7B-case-2/blob/main/results_2024-03-04T05-03-52.331388.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\": 0.29885797902069644,\n\
\ \"acc_stderr\": 0.03205967644162286,\n \"acc_norm\": 0.2997989301581067,\n\
\ \"acc_norm_stderr\": 0.03280338284799219,\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.01539211880501503,\n \"mc2\": 0.3862844409155128,\n\
\ \"mc2_stderr\": 0.014439073256995538\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4334470989761092,\n \"acc_stderr\": 0.014481376224558896,\n\
\ \"acc_norm\": 0.4718430034129693,\n \"acc_norm_stderr\": 0.0145882041051022\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5975901214897431,\n\
\ \"acc_stderr\": 0.00489381489020832,\n \"acc_norm\": 0.7847042421828321,\n\
\ \"acc_norm_stderr\": 0.004101873407354699\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3037037037037037,\n\
\ \"acc_stderr\": 0.03972552884785136,\n \"acc_norm\": 0.3037037037037037,\n\
\ \"acc_norm_stderr\": 0.03972552884785136\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.20394736842105263,\n \"acc_stderr\": 0.0327900040631005,\n\
\ \"acc_norm\": 0.20394736842105263,\n \"acc_norm_stderr\": 0.0327900040631005\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n\
\ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.32452830188679244,\n \"acc_stderr\": 0.028815615713432115,\n\
\ \"acc_norm\": 0.32452830188679244,\n \"acc_norm_stderr\": 0.028815615713432115\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2708333333333333,\n\
\ \"acc_stderr\": 0.03716177437566016,\n \"acc_norm\": 0.2708333333333333,\n\
\ \"acc_norm_stderr\": 0.03716177437566016\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n\
\ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2658959537572254,\n\
\ \"acc_stderr\": 0.033687629322594316,\n \"acc_norm\": 0.2658959537572254,\n\
\ \"acc_norm_stderr\": 0.033687629322594316\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n\
\ \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3191489361702128,\n \"acc_stderr\": 0.03047297336338005,\n\
\ \"acc_norm\": 0.3191489361702128,\n \"acc_norm_stderr\": 0.03047297336338005\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\
\ \"acc_stderr\": 0.04339138322579861,\n \"acc_norm\": 0.30701754385964913,\n\
\ \"acc_norm_stderr\": 0.04339138322579861\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.3103448275862069,\n \"acc_stderr\": 0.038552896163789485,\n\
\ \"acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.038552896163789485\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708628,\n \"\
acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708628\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1746031746031746,\n\
\ \"acc_stderr\": 0.03395490020856109,\n \"acc_norm\": 0.1746031746031746,\n\
\ \"acc_norm_stderr\": 0.03395490020856109\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \
\ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.3258064516129032,\n \"acc_stderr\": 0.026662010578567104,\n \"\
acc_norm\": 0.3258064516129032,\n \"acc_norm_stderr\": 0.026662010578567104\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3103448275862069,\n \"acc_stderr\": 0.03255086769970103,\n \"\
acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.03255086769970103\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\
: 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.3393939393939394,\n \"acc_stderr\": 0.03697442205031596,\n\
\ \"acc_norm\": 0.3393939393939394,\n \"acc_norm_stderr\": 0.03697442205031596\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.30303030303030304,\n \"acc_stderr\": 0.03274287914026868,\n \"\
acc_norm\": 0.30303030303030304,\n \"acc_norm_stderr\": 0.03274287914026868\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.24352331606217617,\n \"acc_stderr\": 0.03097543638684543,\n\
\ \"acc_norm\": 0.24352331606217617,\n \"acc_norm_stderr\": 0.03097543638684543\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2564102564102564,\n \"acc_stderr\": 0.022139081103971545,\n\
\ \"acc_norm\": 0.2564102564102564,\n \"acc_norm_stderr\": 0.022139081103971545\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.29259259259259257,\n \"acc_stderr\": 0.02773896963217609,\n \
\ \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.02773896963217609\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.2773109243697479,\n \"acc_stderr\": 0.02907937453948001,\n \
\ \"acc_norm\": 0.2773109243697479,\n \"acc_norm_stderr\": 0.02907937453948001\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.24503311258278146,\n \"acc_stderr\": 0.035118075718047245,\n \"\
acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.035118075718047245\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.28440366972477066,\n \"acc_stderr\": 0.019342036587702584,\n \"\
acc_norm\": 0.28440366972477066,\n \"acc_norm_stderr\": 0.019342036587702584\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.19907407407407407,\n \"acc_stderr\": 0.02723229846269021,\n \"\
acc_norm\": 0.19907407407407407,\n \"acc_norm_stderr\": 0.02723229846269021\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.28921568627450983,\n \"acc_stderr\": 0.03182231867647553,\n \"\
acc_norm\": 0.28921568627450983,\n \"acc_norm_stderr\": 0.03182231867647553\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.3080168776371308,\n \"acc_stderr\": 0.030052389335605702,\n \
\ \"acc_norm\": 0.3080168776371308,\n \"acc_norm_stderr\": 0.030052389335605702\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3991031390134529,\n\
\ \"acc_stderr\": 0.032867453125679603,\n \"acc_norm\": 0.3991031390134529,\n\
\ \"acc_norm_stderr\": 0.032867453125679603\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2366412213740458,\n \"acc_stderr\": 0.037276735755969195,\n\
\ \"acc_norm\": 0.2366412213740458,\n \"acc_norm_stderr\": 0.037276735755969195\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.34710743801652894,\n \"acc_stderr\": 0.04345724570292535,\n \"\
acc_norm\": 0.34710743801652894,\n \"acc_norm_stderr\": 0.04345724570292535\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.28703703703703703,\n\
\ \"acc_stderr\": 0.04373313040914761,\n \"acc_norm\": 0.28703703703703703,\n\
\ \"acc_norm_stderr\": 0.04373313040914761\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.294478527607362,\n \"acc_stderr\": 0.03581165790474082,\n\
\ \"acc_norm\": 0.294478527607362,\n \"acc_norm_stderr\": 0.03581165790474082\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.043012503996908764,\n\
\ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.043012503996908764\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.36752136752136755,\n\
\ \"acc_stderr\": 0.03158539157745637,\n \"acc_norm\": 0.36752136752136755,\n\
\ \"acc_norm_stderr\": 0.03158539157745637\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.36015325670498083,\n\
\ \"acc_stderr\": 0.017166362471369295,\n \"acc_norm\": 0.36015325670498083,\n\
\ \"acc_norm_stderr\": 0.017166362471369295\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.32947976878612717,\n \"acc_stderr\": 0.025305258131879702,\n\
\ \"acc_norm\": 0.32947976878612717,\n \"acc_norm_stderr\": 0.025305258131879702\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2871508379888268,\n\
\ \"acc_stderr\": 0.015131608849963759,\n \"acc_norm\": 0.2871508379888268,\n\
\ \"acc_norm_stderr\": 0.015131608849963759\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.32679738562091504,\n \"acc_stderr\": 0.02685729466328142,\n\
\ \"acc_norm\": 0.32679738562091504,\n \"acc_norm_stderr\": 0.02685729466328142\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.31189710610932475,\n\
\ \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.31189710610932475,\n\
\ \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.24691358024691357,\n \"acc_stderr\": 0.02399350170904211,\n\
\ \"acc_norm\": 0.24691358024691357,\n \"acc_norm_stderr\": 0.02399350170904211\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2801418439716312,\n \"acc_stderr\": 0.026789172351140245,\n \
\ \"acc_norm\": 0.2801418439716312,\n \"acc_norm_stderr\": 0.026789172351140245\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.25749674054758803,\n\
\ \"acc_stderr\": 0.011167706014904156,\n \"acc_norm\": 0.25749674054758803,\n\
\ \"acc_norm_stderr\": 0.011167706014904156\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.024562204314142314,\n\
\ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.024562204314142314\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2647058823529412,\n \"acc_stderr\": 0.01784808957491322,\n \
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.01784808957491322\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.32727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252089,\n \"acc_norm\": 0.32727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252089\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2163265306122449,\n \"acc_stderr\": 0.026358916334904038,\n\
\ \"acc_norm\": 0.2163265306122449,\n \"acc_norm_stderr\": 0.026358916334904038\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2835820895522388,\n\
\ \"acc_stderr\": 0.031871875379197986,\n \"acc_norm\": 0.2835820895522388,\n\
\ \"acc_norm_stderr\": 0.031871875379197986\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3795180722891566,\n\
\ \"acc_stderr\": 0.03777798822748017,\n \"acc_norm\": 0.3795180722891566,\n\
\ \"acc_norm_stderr\": 0.03777798822748017\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.3391812865497076,\n \"acc_stderr\": 0.036310534964889056,\n\
\ \"acc_norm\": 0.3391812865497076,\n \"acc_norm_stderr\": 0.036310534964889056\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.01539211880501503,\n \"mc2\": 0.3862844409155128,\n\
\ \"mc2_stderr\": 0.014439073256995538\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7040252565114443,\n \"acc_stderr\": 0.012829348226339014\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06368460955269144,\n \
\ \"acc_stderr\": 0.006726213078805713\n }\n}\n```"
repo_url: https://huggingface.co/jisukim8873/falcon-7B-case-2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|arc:challenge|25_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|gsm8k|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hellaswag|10_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T05-03-52.331388.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-04T05-03-52.331388.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- '**/details_harness|winogrande|5_2024-03-04T05-03-52.331388.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-04T05-03-52.331388.parquet'
- config_name: results
data_files:
- split: 2024_03_04T05_03_52.331388
path:
- results_2024-03-04T05-03-52.331388.parquet
- split: latest
path:
- results_2024-03-04T05-03-52.331388.parquet
---
# Dataset Card for Evaluation run of jisukim8873/falcon-7B-case-2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jisukim8873/falcon-7B-case-2](https://huggingface.co/jisukim8873/falcon-7B-case-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_jisukim8873__falcon-7B-case-2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-04T05:03:52.331388](https://huggingface.co/datasets/open-llm-leaderboard/details_jisukim8873__falcon-7B-case-2/blob/main/results_2024-03-04T05-03-52.331388.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": 0.29885797902069644,
"acc_stderr": 0.03205967644162286,
"acc_norm": 0.2997989301581067,
"acc_norm_stderr": 0.03280338284799219,
"mc1": 0.26193390452876375,
"mc1_stderr": 0.01539211880501503,
"mc2": 0.3862844409155128,
"mc2_stderr": 0.014439073256995538
},
"harness|arc:challenge|25": {
"acc": 0.4334470989761092,
"acc_stderr": 0.014481376224558896,
"acc_norm": 0.4718430034129693,
"acc_norm_stderr": 0.0145882041051022
},
"harness|hellaswag|10": {
"acc": 0.5975901214897431,
"acc_stderr": 0.00489381489020832,
"acc_norm": 0.7847042421828321,
"acc_norm_stderr": 0.004101873407354699
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384739,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384739
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3037037037037037,
"acc_stderr": 0.03972552884785136,
"acc_norm": 0.3037037037037037,
"acc_norm_stderr": 0.03972552884785136
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.20394736842105263,
"acc_stderr": 0.0327900040631005,
"acc_norm": 0.20394736842105263,
"acc_norm_stderr": 0.0327900040631005
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.32452830188679244,
"acc_stderr": 0.028815615713432115,
"acc_norm": 0.32452830188679244,
"acc_norm_stderr": 0.028815615713432115
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2708333333333333,
"acc_stderr": 0.03716177437566016,
"acc_norm": 0.2708333333333333,
"acc_norm_stderr": 0.03716177437566016
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.17,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.17,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2658959537572254,
"acc_stderr": 0.033687629322594316,
"acc_norm": 0.2658959537572254,
"acc_norm_stderr": 0.033687629322594316
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.043364327079931785,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.043364327079931785
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3191489361702128,
"acc_stderr": 0.03047297336338005,
"acc_norm": 0.3191489361702128,
"acc_norm_stderr": 0.03047297336338005
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.30701754385964913,
"acc_stderr": 0.04339138322579861,
"acc_norm": 0.30701754385964913,
"acc_norm_stderr": 0.04339138322579861
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.3103448275862069,
"acc_stderr": 0.038552896163789485,
"acc_norm": 0.3103448275862069,
"acc_norm_stderr": 0.038552896163789485
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.26455026455026454,
"acc_stderr": 0.022717467897708628,
"acc_norm": 0.26455026455026454,
"acc_norm_stderr": 0.022717467897708628
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.1746031746031746,
"acc_stderr": 0.03395490020856109,
"acc_norm": 0.1746031746031746,
"acc_norm_stderr": 0.03395490020856109
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.19,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.19,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.3258064516129032,
"acc_stderr": 0.026662010578567104,
"acc_norm": 0.3258064516129032,
"acc_norm_stderr": 0.026662010578567104
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3103448275862069,
"acc_stderr": 0.03255086769970103,
"acc_norm": 0.3103448275862069,
"acc_norm_stderr": 0.03255086769970103
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.3393939393939394,
"acc_stderr": 0.03697442205031596,
"acc_norm": 0.3393939393939394,
"acc_norm_stderr": 0.03697442205031596
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.30303030303030304,
"acc_stderr": 0.03274287914026868,
"acc_norm": 0.30303030303030304,
"acc_norm_stderr": 0.03274287914026868
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.24352331606217617,
"acc_stderr": 0.03097543638684543,
"acc_norm": 0.24352331606217617,
"acc_norm_stderr": 0.03097543638684543
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2564102564102564,
"acc_stderr": 0.022139081103971545,
"acc_norm": 0.2564102564102564,
"acc_norm_stderr": 0.022139081103971545
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.29259259259259257,
"acc_stderr": 0.02773896963217609,
"acc_norm": 0.29259259259259257,
"acc_norm_stderr": 0.02773896963217609
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.2773109243697479,
"acc_stderr": 0.02907937453948001,
"acc_norm": 0.2773109243697479,
"acc_norm_stderr": 0.02907937453948001
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.24503311258278146,
"acc_stderr": 0.035118075718047245,
"acc_norm": 0.24503311258278146,
"acc_norm_stderr": 0.035118075718047245
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.28440366972477066,
"acc_stderr": 0.019342036587702584,
"acc_norm": 0.28440366972477066,
"acc_norm_stderr": 0.019342036587702584
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.19907407407407407,
"acc_stderr": 0.02723229846269021,
"acc_norm": 0.19907407407407407,
"acc_norm_stderr": 0.02723229846269021
},
"harness|hendrycksTest-high_school_us_history|5": {
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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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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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},
"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.06368460955269144,
"acc_stderr": 0.006726213078805713
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### 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. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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### Recommendations
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## Citation [optional]
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aleh/aims_segm_crop | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 630264241.0
num_examples: 25
download_size: 142370545
dataset_size: 630264241.0
---
# Dataset Card for "aims_segm_crop"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
senhorsapo/ram | ---
license: openrail
---
|
reginaboateng/Bioasq7b_list | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: train
num_bytes: 14557028
num_examples: 8598
download_size: 2877034
dataset_size: 14557028
---
# Dataset Card for "Bioasq7b_list"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ayymen/Weblate-Translations | ---
configs:
- config_name: en-lk
data_files: en-lk.tsv
- config_name: en-en-rAU
data_files: en-en-rAU.tsv
- config_name: en-hy-rAM
data_files: en-hy-rAM.tsv
- config_name: en-qt
data_files: en-qt.tsv
- config_name: en-se
data_files: en-se.tsv
- config_name: en-en_AU
data_files: en-en_AU.tsv
- config_name: en-in
data_files: en-in.tsv
- config_name: en_US-id
data_files: en_US-id.tsv
- config_name: en-ajp
data_files: en-ajp.tsv
- config_name: en-en_US_rude
data_files: en-en_US_rude.tsv
- config_name: en_GB-sw
data_files: en_GB-sw.tsv
- config_name: en_GB-tzm
data_files: en_GB-tzm.tsv
- config_name: dev-pt
data_files: dev-pt.tsv
- config_name: de-nb_NO
data_files: de-nb_NO.tsv
- config_name: en_devel-bn_BD
data_files: en_devel-bn_BD.tsv
- config_name: messages-fr
data_files: messages-fr.tsv
- config_name: en-de-CH
data_files: en-de-CH.tsv
- config_name: en-gu_IN
data_files: en-gu_IN.tsv
- config_name: en-be_BY
data_files: en-be_BY.tsv
- config_name: eo-sk
data_files: eo-sk.tsv
- config_name: en-brx
data_files: en-brx.tsv
- config_name: en-en_US
data_files: en-en_US.tsv
- config_name: en_GB-an
data_files: en_GB-an.tsv
- config_name: en-korean
data_files: en-korean.tsv
- config_name: en_GB-fr-FR
data_files: en_GB-fr-FR.tsv
- config_name: en_devel-si
data_files: en_devel-si.tsv
- config_name: en_US-sr_Cyrl
data_files: en_US-sr_Cyrl.tsv
- config_name: en-fr@formal
data_files: en-fr@formal.tsv
- config_name: en_devel-zh_tw
data_files: en_devel-zh_tw.tsv
- config_name: en-en_ud
data_files: en-en_ud.tsv
- config_name: en_GB-bi
data_files: en_GB-bi.tsv
- config_name: en-sq_AL
data_files: en-sq_AL.tsv
- config_name: en-README_zh-CN
data_files: en-README_zh-CN.tsv
- config_name: en_US-ml_IN
data_files: en_US-ml_IN.tsv
- config_name: nb_NO-nn
data_files: nb_NO-nn.tsv
- config_name: en_devel-es_419
data_files: en_devel-es_419.tsv
- config_name: en-de-DE
data_files: en-de-DE.tsv
- config_name: en-dua
data_files: en-dua.tsv
- config_name: en-gu-rIN
data_files: en-gu-rIN.tsv
- config_name: en-ty
data_files: en-ty.tsv
- config_name: nl-pl
data_files: nl-pl.tsv
- config_name: en_US-bo
data_files: en_US-bo.tsv
- config_name: en_devel-ru_RU
data_files: en_devel-ru_RU.tsv
- config_name: en_GB-cy_GB
data_files: en_GB-cy_GB.tsv
- config_name: en_US-zh-TW
data_files: en_US-zh-TW.tsv
- config_name: en_US-zh-hk
data_files: en_US-zh-hk.tsv
- config_name: en-DE
data_files: en-DE.tsv
- config_name: en_US-lzh
data_files: en_US-lzh.tsv
- config_name: sv-sma
data_files: sv-sma.tsv
- config_name: en_GB-fi_FI
data_files: en_GB-fi_FI.tsv
- config_name: en_US-zu
data_files: en_US-zu.tsv
- config_name: en_devel-mr
data_files: en_devel-mr.tsv
- config_name: en_US-he-IL
data_files: en_US-he-IL.tsv
- config_name: en_GB-fur
data_files: en_GB-fur.tsv
- config_name: en-fr_CH
data_files: en-fr_CH.tsv
- config_name: en-en-CA
data_files: en-en-CA.tsv
- config_name: en-ro_MD
data_files: en-ro_MD.tsv
- config_name: en_US-yue_HK
data_files: en_US-yue_HK.tsv
- config_name: es-mr
data_files: es-mr.tsv
- config_name: en_GB-ace
data_files: en_GB-ace.tsv
- config_name: en_GB-lt
data_files: en_GB-lt.tsv
- config_name: en-es-rES
data_files: en-es-rES.tsv
- config_name: en-ksh
data_files: en-ksh.tsv
- config_name: en_GB-ti
data_files: en_GB-ti.tsv
- config_name: en-zh-rSG
data_files: en-zh-rSG.tsv
- config_name: en-ms_Arab
data_files: en-ms_Arab.tsv
- config_name: en-README_CZ
data_files: en-README_CZ.tsv
- config_name: en-ug-CN
data_files: en-ug-CN.tsv
- config_name: en-ar-rYE
data_files: en-ar-rYE.tsv
- config_name: en-pk
data_files: en-pk.tsv
- config_name: en_US-pt
data_files: en_US-pt.tsv
- config_name: en_devel-pt-br
data_files: en_devel-pt-br.tsv
- config_name: en-de_formal
data_files: en-de_formal.tsv
- config_name: en-zh_TW
data_files: en-zh_TW.tsv
- config_name: en-hu-rHU
data_files: en-hu-rHU.tsv
- config_name: en-lv-LV
data_files: en-lv-LV.tsv
- config_name: en-hr_HR
data_files: en-hr_HR.tsv
- config_name: en-en_devel
data_files: en-en_devel.tsv
- config_name: en-ka
data_files: en-ka.tsv
- config_name: en_GB-da_DK
data_files: en_GB-da_DK.tsv
- config_name: en-ar-AR
data_files: en-ar-AR.tsv
- config_name: en-om
data_files: en-om.tsv
- config_name: en_US-id-ID
data_files: en_US-id-ID.tsv
- config_name: en-cs_CZ
data_files: en-cs_CZ.tsv
- config_name: it-es_ES
data_files: it-es_ES.tsv
- config_name: en-zh_HK
data_files: en-zh_HK.tsv
- config_name: dev-ko
data_files: dev-ko.tsv
- config_name: en-cr
data_files: en-cr.tsv
- config_name: en-sr_Cyrl
data_files: en-sr_Cyrl.tsv
- config_name: en-nl_BE
data_files: en-nl_BE.tsv
- config_name: en_GB-zh-rTW
data_files: en_GB-zh-rTW.tsv
- config_name: en-da-DK
data_files: en-da-DK.tsv
- config_name: en-ang
data_files: en-ang.tsv
- config_name: en-ur-IN
data_files: en-ur-IN.tsv
- config_name: en-HU
data_files: en-HU.tsv
- config_name: en-kw
data_files: en-kw.tsv
- config_name: en_GB-fo
data_files: en_GB-fo.tsv
- config_name: en-sr-SP
data_files: en-sr-SP.tsv
- config_name: en-pl
data_files: en-pl.tsv
- config_name: en-or
data_files: en-or.tsv
- config_name: en-en-gb
data_files: en-en-gb.tsv
- config_name: en-en
data_files: en-en.tsv
- config_name: en_GB-fa_IR
data_files: en_GB-fa_IR.tsv
- config_name: en-bn-IN
data_files: en-bn-IN.tsv
- config_name: en-pl_pl
data_files: en-pl_pl.tsv
- config_name: en_US-ro_RO
data_files: en_US-ro_RO.tsv
- config_name: en-es_mx
data_files: en-es_mx.tsv
- config_name: en-kk_KZ
data_files: en-kk_KZ.tsv
- config_name: en-ab
data_files: en-ab.tsv
- config_name: en_UK-de_DE
data_files: en_UK-de_DE.tsv
- config_name: eo-de
data_files: eo-de.tsv
- config_name: en_US-fil
data_files: en_US-fil.tsv
- config_name: en-bp
data_files: en-bp.tsv
- config_name: en-ta_IN
data_files: en-ta_IN.tsv
- config_name: en-round
data_files: en-round.tsv
- config_name: en-gd
data_files: en-gd.tsv
- config_name: en_US-en@uwu
data_files: en_US-en@uwu.tsv
- config_name: en-dum
data_files: en-dum.tsv
- config_name: en-ja_JP
data_files: en-ja_JP.tsv
- config_name: en-ryu
data_files: en-ryu.tsv
- config_name: en-b+en+001
data_files: en-b+en+001.tsv
- config_name: en-en-US
data_files: en-en-US.tsv
- config_name: en-sl_SI
data_files: en-sl_SI.tsv
- config_name: de-it
data_files: de-it.tsv
- config_name: en_GB-sr_RS
data_files: en_GB-sr_RS.tsv
- config_name: en_US-da
data_files: en_US-da.tsv
- config_name: en_GB-tk
data_files: en_GB-tk.tsv
- config_name: en-bn
data_files: en-bn.tsv
- config_name: en_devel-es_bo
data_files: en_devel-es_bo.tsv
- config_name: en-ja_CARES
data_files: en-ja_CARES.tsv
- config_name: en-km-KH
data_files: en-km-KH.tsv
- config_name: en_US-de_DE
data_files: en_US-de_DE.tsv
- config_name: en_US-hu_HU
data_files: en_US-hu_HU.tsv
- config_name: en-ta-rIN
data_files: en-ta-rIN.tsv
- config_name: en_US-ml
data_files: en_US-ml.tsv
- config_name: en-sr_RS
data_files: en-sr_RS.tsv
- config_name: en_US-eu
data_files: en_US-eu.tsv
- config_name: pl-es
data_files: pl-es.tsv
- config_name: en_US-ka
data_files: en_US-ka.tsv
- config_name: en-bulgarian
data_files: en-bulgarian.tsv
- config_name: fr-en
data_files: fr-en.tsv
- config_name: en_devel-nb-rNO
data_files: en_devel-nb-rNO.tsv
- config_name: en_GB-ce
data_files: en_GB-ce.tsv
- config_name: en_US-bs
data_files: en_US-bs.tsv
- config_name: en-en@uwu
data_files: en-en@uwu.tsv
- config_name: en_GB-nn
data_files: en_GB-nn.tsv
- config_name: en-pa_PK
data_files: en-pa_PK.tsv
- config_name: en-wae
data_files: en-wae.tsv
- config_name: en-ar_EG
data_files: en-ar_EG.tsv
- config_name: en_GB-lt_LT
data_files: en_GB-lt_LT.tsv
- config_name: en-zh-Hant-HK
data_files: en-zh-Hant-HK.tsv
- config_name: messages-de
data_files: messages-de.tsv
- config_name: en-ur_IN
data_files: en-ur_IN.tsv
- config_name: en-in-rID
data_files: en-in-rID.tsv
- config_name: en-lo-LA
data_files: en-lo-LA.tsv
- config_name: en-el-rGR
data_files: en-el-rGR.tsv
- config_name: en-es-ES
data_files: en-es-ES.tsv
- config_name: en_devel-et
data_files: en_devel-et.tsv
- config_name: en-fr-rCH
data_files: en-fr-rCH.tsv
- config_name: en-en_CA
data_files: en-en_CA.tsv
- config_name: en-b+uz+Latn
data_files: en-b+uz+Latn.tsv
- config_name: en_GB-tig
data_files: en_GB-tig.tsv
- config_name: en_GB-hi_IN
data_files: en_GB-hi_IN.tsv
- config_name: de-pl
data_files: de-pl.tsv
- config_name: en-zh-rCN
data_files: en-zh-rCN.tsv
- config_name: en-hi-rIN
data_files: en-hi-rIN.tsv
- config_name: en-ba
data_files: en-ba.tsv
- config_name: en-fy
data_files: en-fy.tsv
- config_name: en-el-GR
data_files: en-el-GR.tsv
- config_name: en-tum
data_files: en-tum.tsv
- config_name: en-ru-RU
data_files: en-ru-RU.tsv
- config_name: en_US-fa
data_files: en_US-fa.tsv
- config_name: en_GB-ka
data_files: en_GB-ka.tsv
- config_name: es-nb-rNO
data_files: es-nb-rNO.tsv
- config_name: en_US-ckb
data_files: en_US-ckb.tsv
- config_name: en-hi_IN
data_files: en-hi_IN.tsv
- config_name: eo-pa
data_files: eo-pa.tsv
- config_name: en_devel-zh_TW
data_files: en_devel-zh_TW.tsv
- config_name: en_GB-ch
data_files: en_GB-ch.tsv
- config_name: en-sdh
data_files: en-sdh.tsv
- config_name: en-lzh
data_files: en-lzh.tsv
- config_name: en-zh_HANS-CN
data_files: en-zh_HANS-CN.tsv
- config_name: en-li
data_files: en-li.tsv
- config_name: en_devel-zh_cn
data_files: en_devel-zh_cn.tsv
- config_name: en_GB-mk
data_files: en_GB-mk.tsv
- config_name: en_GB-ay
data_files: en_GB-ay.tsv
- config_name: en-sq-rAL
data_files: en-sq-rAL.tsv
- config_name: en-nl_TND
data_files: en-nl_TND.tsv
- config_name: en-th
data_files: en-th.tsv
- config_name: messages-id
data_files: messages-id.tsv
- config_name: en-bo
data_files: en-bo.tsv
- config_name: en-hy
data_files: en-hy.tsv
- config_name: en_US-gd
data_files: en_US-gd.tsv
- config_name: en-tok
data_files: en-tok.tsv
- config_name: pt_BR-en
data_files: pt_BR-en.tsv
- config_name: fr-pt
data_files: fr-pt.tsv
- config_name: en-bs-rBA
data_files: en-bs-rBA.tsv
- config_name: en-zh-hant
data_files: en-zh-hant.tsv
- config_name: en_US-fr
data_files: en_US-fr.tsv
- config_name: en-eu-ES
data_files: en-eu-ES.tsv
- config_name: en-lv_LV
data_files: en-lv_LV.tsv
- config_name: und-fr
data_files: und-fr.tsv
- config_name: en-af-rZA
data_files: en-af-rZA.tsv
- config_name: en-da
data_files: en-da.tsv
- config_name: en-os
data_files: en-os.tsv
- config_name: en-fr-CH
data_files: en-fr-CH.tsv
- config_name: en-es_MX
data_files: en-es_MX.tsv
- config_name: nl-bg
data_files: nl-bg.tsv
- config_name: en_GB-ckb
data_files: en_GB-ckb.tsv
- config_name: en-ar-rEG
data_files: en-ar-rEG.tsv
- config_name: en_US-mr
data_files: en_US-mr.tsv
- config_name: en_US-cs-CZ
data_files: en_US-cs-CZ.tsv
- config_name: en_devel-fi
data_files: en_devel-fi.tsv
- config_name: en-mhr
data_files: en-mhr.tsv
- config_name: en-no-rNO
data_files: en-no-rNO.tsv
- config_name: en-it_it
data_files: en-it_it.tsv
- config_name: en-ar-rSA
data_files: en-ar-rSA.tsv
- config_name: en_GB-nso
data_files: en_GB-nso.tsv
- config_name: en-ti
data_files: en-ti.tsv
- config_name: en-iw_HE
data_files: en-iw_HE.tsv
- config_name: en-szl
data_files: en-szl.tsv
- config_name: en_GB-ba
data_files: en_GB-ba.tsv
- config_name: en_devel-cs
data_files: en_devel-cs.tsv
- config_name: en_GB-pl_PL
data_files: en_GB-pl_PL.tsv
- config_name: en-ta_LK
data_files: en-ta_LK.tsv
- config_name: en-uz@latin
data_files: en-uz@latin.tsv
- config_name: en-el
data_files: en-el.tsv
- config_name: en_GB-cs
data_files: en_GB-cs.tsv
- config_name: en-bul_BG
data_files: en-bul_BG.tsv
- config_name: en-fa_IR
data_files: en-fa_IR.tsv
- config_name: en-gsw
data_files: en-gsw.tsv
- config_name: en-ko-KR
data_files: en-ko-KR.tsv
- config_name: en-bs_BA
data_files: en-bs_BA.tsv
- config_name: en_GB-wo
data_files: en_GB-wo.tsv
- config_name: en_devel-it
data_files: en_devel-it.tsv
- config_name: en_US-bn
data_files: en_US-bn.tsv
- config_name: en_devel-pl
data_files: en_devel-pl.tsv
- config_name: en-rm
data_files: en-rm.tsv
- config_name: en-night
data_files: en-night.tsv
- config_name: eo-ca
data_files: eo-ca.tsv
- config_name: en_US-ps
data_files: en_US-ps.tsv
- config_name: en_GB-sd
data_files: en_GB-sd.tsv
- config_name: en-th-TH
data_files: en-th-TH.tsv
- config_name: en-sv-rSE
data_files: en-sv-rSE.tsv
- config_name: en-b+zh+Hans
data_files: en-b+zh+Hans.tsv
- config_name: en_devel-uk
data_files: en_devel-uk.tsv
- config_name: en_US-it_IT
data_files: en_US-it_IT.tsv
- config_name: en-b+hrx
data_files: en-b+hrx.tsv
- config_name: en-my
data_files: en-my.tsv
- config_name: en_GB-sc
data_files: en_GB-sc.tsv
- config_name: en-de_DE_rude
data_files: en-de_DE_rude.tsv
- config_name: en_GB-ff
data_files: en_GB-ff.tsv
- config_name: en_devel-nl
data_files: en_devel-nl.tsv
- config_name: en-shn
data_files: en-shn.tsv
- config_name: en_GB-ca
data_files: en_GB-ca.tsv
- config_name: en-hu_HU
data_files: en-hu_HU.tsv
- config_name: ru-be
data_files: ru-be.tsv
- config_name: es-ml
data_files: es-ml.tsv
- config_name: en_GB-na
data_files: en_GB-na.tsv
- config_name: en_devel-ja
data_files: en_devel-ja.tsv
- config_name: en-pt-rPT-v26
data_files: en-pt-rPT-v26.tsv
- config_name: en_devel-pt_BR
data_files: en_devel-pt_BR.tsv
- config_name: en_US-ar_AA
data_files: en_US-ar_AA.tsv
- config_name: en_US-en_GB
data_files: en_US-en_GB.tsv
- config_name: en-de_FORM
data_files: en-de_FORM.tsv
- config_name: en_US-et
data_files: en_US-et.tsv
- config_name: pl-it
data_files: pl-it.tsv
- config_name: messages-ru
data_files: messages-ru.tsv
- config_name: en_devel-en
data_files: en_devel-en.tsv
- config_name: en-te_IN
data_files: en-te_IN.tsv
- config_name: en_US-it-IT
data_files: en_US-it-IT.tsv
- config_name: en-zh-rMO
data_files: en-zh-rMO.tsv
- config_name: en-fy-NL
data_files: en-fy-NL.tsv
- config_name: en-iw-rIL
data_files: en-iw-rIL.tsv
- config_name: en-zh-Hant
data_files: en-zh-Hant.tsv
- config_name: en-es_uy
data_files: en-es_uy.tsv
- config_name: en_GB-or
data_files: en_GB-or.tsv
- config_name: en-tt
data_files: en-tt.tsv
- config_name: de-pt
data_files: de-pt.tsv
- config_name: en-zh-Hans
data_files: en-zh-Hans.tsv
- config_name: en-ar-TN
data_files: en-ar-TN.tsv
- config_name: en_US-si_LK
data_files: en_US-si_LK.tsv
- config_name: en-so
data_files: en-so.tsv
- config_name: en_GB-csb
data_files: en_GB-csb.tsv
- config_name: en-fr-CA
data_files: en-fr-CA.tsv
- config_name: en-es_BO
data_files: en-es_BO.tsv
- config_name: en_devel-es_pa
data_files: en_devel-es_pa.tsv
- config_name: en-vi-VN
data_files: en-vi-VN.tsv
- config_name: en_devel-sw
data_files: en_devel-sw.tsv
- config_name: en-es-rMX
data_files: en-es-rMX.tsv
- config_name: en-eu-rES
data_files: en-eu-rES.tsv
- config_name: en_GB-pi
data_files: en_GB-pi.tsv
- config_name: en_devel-bg
data_files: en_devel-bg.tsv
- config_name: en-ja-JP
data_files: en-ja-JP.tsv
- config_name: en_US-uk
data_files: en_US-uk.tsv
- config_name: en_GB-km
data_files: en_GB-km.tsv
- config_name: en_US-ko
data_files: en_US-ko.tsv
- config_name: en-gmh
data_files: en-gmh.tsv
- config_name: en_US-hy
data_files: en_US-hy.tsv
- config_name: en_GB-ml
data_files: en_GB-ml.tsv
- config_name: en-bn-rIN
data_files: en-bn-rIN.tsv
- config_name: en-ach
data_files: en-ach.tsv
- config_name: en-pt-rBR-v26
data_files: en-pt-rBR-v26.tsv
- config_name: en_US-zh
data_files: en_US-zh.tsv
- config_name: en-sw-rKE
data_files: en-sw-rKE.tsv
- config_name: en_GB-ha
data_files: en_GB-ha.tsv
- config_name: en-en-rGB
data_files: en-en-rGB.tsv
- config_name: en_devel-pt
data_files: en_devel-pt.tsv
- config_name: en-no_NB
data_files: en-no_NB.tsv
- config_name: en-no_NO
data_files: en-no_NO.tsv
- config_name: en-es_es
data_files: en-es_es.tsv
- config_name: en-kk
data_files: en-kk.tsv
- config_name: en-bm
data_files: en-bm.tsv
- config_name: en-pl-PL
data_files: en-pl-PL.tsv
- config_name: en_GB-id
data_files: en_GB-id.tsv
- config_name: en-sr-Latn
data_files: en-sr-Latn.tsv
- config_name: en_US-ms
data_files: en_US-ms.tsv
- config_name: en-et_ET
data_files: en-et_ET.tsv
- config_name: en-b+es+419
data_files: en-b+es+419.tsv
- config_name: en_GB-kw
data_files: en_GB-kw.tsv
- config_name: en-no
data_files: en-no.tsv
- config_name: en-wa
data_files: en-wa.tsv
- config_name: en-ber
data_files: en-ber.tsv
- config_name: en_US-es_MX
data_files: en_US-es_MX.tsv
- config_name: en-de_1901
data_files: en-de_1901.tsv
- config_name: en-ja-rJP
data_files: en-ja-rJP.tsv
- config_name: en_US-uk_UA
data_files: en_US-uk_UA.tsv
- config_name: en_US-ja_JP
data_files: en_US-ja_JP.tsv
- config_name: en-b+fr
data_files: en-b+fr.tsv
- config_name: en-pt-br
data_files: en-pt-br.tsv
- config_name: en-te
data_files: en-te.tsv
- config_name: en-np
data_files: en-np.tsv
- config_name: en_GB-gu
data_files: en_GB-gu.tsv
- config_name: en_GB-ki
data_files: en_GB-ki.tsv
- config_name: en-kab-KAB
data_files: en-kab-KAB.tsv
- config_name: de-fr
data_files: de-fr.tsv
- config_name: en-ru_old
data_files: en-ru_old.tsv
- config_name: en_devel-es_do
data_files: en_devel-es_do.tsv
- config_name: en-ua
data_files: en-ua.tsv
- config_name: en-et_EE
data_files: en-et_EE.tsv
- config_name: ia-it
data_files: ia-it.tsv
- config_name: en_GB-ro
data_files: en_GB-ro.tsv
- config_name: en_US-pt-rPT
data_files: en_US-pt-rPT.tsv
- config_name: en-ur_PK
data_files: en-ur_PK.tsv
- config_name: en-pa-rPK
data_files: en-pa-rPK.tsv
- config_name: en-vec
data_files: en-vec.tsv
- config_name: en-nl-rBE
data_files: en-nl-rBE.tsv
- config_name: en-lv
data_files: en-lv.tsv
- config_name: en-ar-rBH
data_files: en-ar-rBH.tsv
- config_name: en-an
data_files: en-an.tsv
- config_name: en_US-sr
data_files: en_US-sr.tsv
- config_name: en-Ukrainian
data_files: en-Ukrainian.tsv
- config_name: en_US-mk
data_files: en_US-mk.tsv
- config_name: en_GB-br
data_files: en_GB-br.tsv
- config_name: en-de@informal
data_files: en-de@informal.tsv
- config_name: en-dz
data_files: en-dz.tsv
- config_name: en_US-he_IL
data_files: en_US-he_IL.tsv
- config_name: en_GB-mr
data_files: en_GB-mr.tsv
- config_name: en-cs-CARES
data_files: en-cs-CARES.tsv
- config_name: en_US-hi_IN
data_files: en_US-hi_IN.tsv
- config_name: en_US-ro
data_files: en_US-ro.tsv
- config_name: en_US-fr_CA
data_files: en_US-fr_CA.tsv
- config_name: en-as
data_files: en-as.tsv
- config_name: en_GB-ro_MD
data_files: en_GB-ro_MD.tsv
- config_name: en_US-lt-LT
data_files: en_US-lt-LT.tsv
- config_name: fr-ca
data_files: fr-ca.tsv
- config_name: en-be_Latn
data_files: en-be_Latn.tsv
- config_name: en-en-AU
data_files: en-en-AU.tsv
- config_name: en_US-fr_FR
data_files: en_US-fr_FR.tsv
- config_name: en-de-de
data_files: en-de-de.tsv
- config_name: en-nds
data_files: en-nds.tsv
- config_name: en_US-ja
data_files: en_US-ja.tsv
- config_name: en-es-AR
data_files: en-es-AR.tsv
- config_name: en-ms
data_files: en-ms.tsv
- config_name: en-zh-CHS
data_files: en-zh-CHS.tsv
- config_name: en_devel-bs
data_files: en_devel-bs.tsv
- config_name: en-arn
data_files: en-arn.tsv
- config_name: zh_Hans-en
data_files: zh_Hans-en.tsv
- config_name: en-co
data_files: en-co.tsv
- config_name: en-uz_Latn
data_files: en-uz_Latn.tsv
- config_name: en-cs-rCZ
data_files: en-cs-rCZ.tsv
- config_name: en-ku
data_files: en-ku.tsv
- config_name: en-ha
data_files: en-ha.tsv
- config_name: en-de-zuerich-lernt
data_files: en-de-zuerich-lernt.tsv
- config_name: en_US-be
data_files: en_US-be.tsv
- config_name: en-tr
data_files: en-tr.tsv
- config_name: en-ru_ru
data_files: en-ru_ru.tsv
- config_name: en-kl
data_files: en-kl.tsv
- config_name: en-it
data_files: en-it.tsv
- config_name: en-b+be+Latn
data_files: en-b+be+Latn.tsv
- config_name: en_devel-mk
data_files: en_devel-mk.tsv
- config_name: en_US-vi
data_files: en_US-vi.tsv
- config_name: en-zh_CMN-HANT
data_files: en-zh_CMN-HANT.tsv
- config_name: en-mnw
data_files: en-mnw.tsv
- config_name: en_US-sv-SE
data_files: en_US-sv-SE.tsv
- config_name: en-gum
data_files: en-gum.tsv
- config_name: en-my_MM
data_files: en-my_MM.tsv
- config_name: en_GB-mk_MK
data_files: en_GB-mk_MK.tsv
- config_name: en_devel-es_ec
data_files: en_devel-es_ec.tsv
- config_name: en_US-ne
data_files: en_US-ne.tsv
- config_name: nl-zh_Hans
data_files: nl-zh_Hans.tsv
- config_name: en-zh_hans
data_files: en-zh_hans.tsv
- config_name: en-sr-rCS
data_files: en-sr-rCS.tsv
- config_name: en-es_NI
data_files: en-es_NI.tsv
- config_name: en_GB-bs
data_files: en_GB-bs.tsv
- config_name: en_GB-tr_TR
data_files: en_GB-tr_TR.tsv
- config_name: ru-en
data_files: ru-en.tsv
- config_name: en_US-my
data_files: en_US-my.tsv
- config_name: en-ia
data_files: en-ia.tsv
- config_name: en-hu-HU
data_files: en-hu-HU.tsv
- config_name: en-nn_NO
data_files: en-nn_NO.tsv
- config_name: en_GB-es_419
data_files: en_GB-es_419.tsv
- config_name: en-ca-rES
data_files: en-ca-rES.tsv
- config_name: en_US-zh-CN
data_files: en_US-zh-CN.tsv
- config_name: en_US-tzm
data_files: en_US-tzm.tsv
- config_name: en-it_CARES
data_files: en-it_CARES.tsv
- config_name: en_GB-he
data_files: en_GB-he.tsv
- config_name: en_US-sn
data_files: en_US-sn.tsv
- config_name: en-ml_IN
data_files: en-ml_IN.tsv
- config_name: en-guc
data_files: en-guc.tsv
- config_name: zh_Hans-ru
data_files: zh_Hans-ru.tsv
- config_name: en-csb
data_files: en-csb.tsv
- config_name: en-nan
data_files: en-nan.tsv
- config_name: en-fa-IR
data_files: en-fa-IR.tsv
- config_name: en_US-en_CA
data_files: en_US-en_CA.tsv
- config_name: en_GB-ar
data_files: en_GB-ar.tsv
- config_name: en_GB-ia_FR
data_files: en_GB-ia_FR.tsv
- config_name: en_US-es-MX
data_files: en_US-es-MX.tsv
- config_name: en_devel-el
data_files: en_devel-el.tsv
- config_name: en_GB-ach
data_files: en_GB-ach.tsv
- config_name: en-Italian
data_files: en-Italian.tsv
- config_name: en_devel-az
data_files: en_devel-az.tsv
- config_name: eo-ru
data_files: eo-ru.tsv
- config_name: en-es_US
data_files: en-es_US.tsv
- config_name: en_devel-cy
data_files: en_devel-cy.tsv
- config_name: en-es-mx
data_files: en-es-mx.tsv
- config_name: en-en-rCA
data_files: en-en-rCA.tsv
- config_name: en-kn-IN
data_files: en-kn-IN.tsv
- config_name: en_devel-zh_CN
data_files: en_devel-zh_CN.tsv
- config_name: en_US-lt_LT
data_files: en_US-lt_LT.tsv
- config_name: en_GB-id_ID
data_files: en_GB-id_ID.tsv
- config_name: en-mt
data_files: en-mt.tsv
- config_name: en-bar
data_files: en-bar.tsv
- config_name: en-kr
data_files: en-kr.tsv
- config_name: en_GB-de-DE
data_files: en_GB-de-DE.tsv
- config_name: en-zgh
data_files: en-zgh.tsv
default: true
- config_name: en-german
data_files: en-german.tsv
- config_name: en-de_ch
data_files: en-de_ch.tsv
- config_name: en_devel-hy
data_files: en_devel-hy.tsv
- config_name: en_GB-hr
data_files: en_GB-hr.tsv
- config_name: en_GB-ca_AD
data_files: en_GB-ca_AD.tsv
- config_name: en-b+ca+VALENCIA
data_files: en-b+ca+VALENCIA.tsv
- config_name: en-rw
data_files: en-rw.tsv
- config_name: en-fil-FIL
data_files: en-fil-FIL.tsv
- config_name: it-de
data_files: it-de.tsv
- config_name: en_US-es-rMX
data_files: en_US-es-rMX.tsv
- config_name: en-sk-SK
data_files: en-sk-SK.tsv
- config_name: en-my-MM
data_files: en-my-MM.tsv
- config_name: en-es_ve
data_files: en-es_ve.tsv
- config_name: en-fra-rFR
data_files: en-fra-rFR.tsv
- config_name: en_GB-gv
data_files: en_GB-gv.tsv
- config_name: en-ml-IN
data_files: en-ml-IN.tsv
- config_name: en_US-zh-rHK
data_files: en_US-zh-rHK.tsv
- config_name: en-fur
data_files: en-fur.tsv
- config_name: en_GB-sv
data_files: en_GB-sv.tsv
- config_name: en-ne-rNP
data_files: en-ne-rNP.tsv
- config_name: en_GB-fr
data_files: en_GB-fr.tsv
- config_name: en_US-qya
data_files: en_US-qya.tsv
- config_name: en-ja_KS
data_files: en-ja_KS.tsv
- config_name: en-en_uwu_x
data_files: en-en_uwu_x.tsv
- config_name: en-zh_CN
data_files: en-zh_CN.tsv
- config_name: en-az_AZ
data_files: en-az_AZ.tsv
- config_name: en-bem
data_files: en-bem.tsv
- config_name: en-ars
data_files: en-ars.tsv
- config_name: en-xh
data_files: en-xh.tsv
- config_name: en_US-zh_Hant_HK
data_files: en_US-zh_Hant_HK.tsv
- config_name: en_US-en-rGB
data_files: en_US-en-rGB.tsv
- config_name: en-pam
data_files: en-pam.tsv
- config_name: en_devel-zh-rCN
data_files: en_devel-zh-rCN.tsv
- config_name: en-zh_LATN@pinyin
data_files: en-zh_LATN@pinyin.tsv
- config_name: en_US-en_NZ
data_files: en_US-en_NZ.tsv
- config_name: en-nb_no
data_files: en-nb_no.tsv
- config_name: en-bn-rBD
data_files: en-bn-rBD.tsv
- config_name: en-pl_PL
data_files: en-pl_PL.tsv
- config_name: en-romanian
data_files: en-romanian.tsv
- config_name: en_US-ja_KANJI
data_files: en_US-ja_KANJI.tsv
- config_name: en_US-zh-rCN
data_files: en_US-zh-rCN.tsv
- config_name: en-ca_es
data_files: en-ca_es.tsv
- config_name: en-de_de
data_files: en-de_de.tsv
- config_name: en-rom
data_files: en-rom.tsv
- config_name: en_devel-lv
data_files: en_devel-lv.tsv
- config_name: en-ro
data_files: en-ro.tsv
- config_name: en_US-th-TH
data_files: en_US-th-TH.tsv
- config_name: en_GB-wal
data_files: en_GB-wal.tsv
- config_name: en_US-fi-FI
data_files: en_US-fi-FI.tsv
- config_name: en-ar_AR
data_files: en-ar_AR.tsv
- config_name: en_US-el
data_files: en_US-el.tsv
- config_name: en_GB-chr
data_files: en_GB-chr.tsv
- config_name: en-pbb
data_files: en-pbb.tsv
- config_name: en-ar-rXB
data_files: en-ar-rXB.tsv
- config_name: en-tzm
data_files: en-tzm.tsv
- config_name: en-mr-rIN
data_files: en-mr-rIN.tsv
- config_name: en-ms-rMY
data_files: en-ms-rMY.tsv
- config_name: en-apc
data_files: en-apc.tsv
- config_name: en_GB-fi
data_files: en_GB-fi.tsv
- config_name: en_US-hi
data_files: en_US-hi.tsv
- config_name: en-hz
data_files: en-hz.tsv
- config_name: en_GB-mi
data_files: en_GB-mi.tsv
- config_name: en-sai
data_files: en-sai.tsv
- config_name: en-ig
data_files: en-ig.tsv
- config_name: en-en_Shaw
data_files: en-en_Shaw.tsv
- config_name: en_US-fa_IR
data_files: en_US-fa_IR.tsv
- config_name: en-mr
data_files: en-mr.tsv
- config_name: en-pl_PL_rude
data_files: en-pl_PL_rude.tsv
- config_name: en-cv
data_files: en-cv.tsv
- config_name: messages-ar
data_files: messages-ar.tsv
- config_name: en-ko_KO
data_files: en-ko_KO.tsv
- config_name: en_US-zh-hans
data_files: en_US-zh-hans.tsv
- config_name: en-ga-IE
data_files: en-ga-IE.tsv
- config_name: en-am
data_files: en-am.tsv
- config_name: en-ug
data_files: en-ug.tsv
- config_name: en-af_ZA
data_files: en-af_ZA.tsv
- config_name: en-ES
data_files: en-ES.tsv
- config_name: en_US-ru_RU
data_files: en_US-ru_RU.tsv
- config_name: en_GB-lv
data_files: en_GB-lv.tsv
- config_name: en-yi
data_files: en-yi.tsv
- config_name: en_GB-pl
data_files: en_GB-pl.tsv
- config_name: en_GB-tl
data_files: en_GB-tl.tsv
- config_name: en-km
data_files: en-km.tsv
- config_name: en-azb
data_files: en-azb.tsv
- config_name: en_devel-fr
data_files: en_devel-fr.tsv
- config_name: en-pa-PK
data_files: en-pa-PK.tsv
- config_name: en-tn
data_files: en-tn.tsv
- config_name: en-mjw
data_files: en-mjw.tsv
- config_name: en-frs
data_files: en-frs.tsv
- config_name: en-it-IT
data_files: en-it-IT.tsv
- config_name: en-ro_RO
data_files: en-ro_RO.tsv
- config_name: en_US-nl_NL
data_files: en_US-nl_NL.tsv
- config_name: en-ht
data_files: en-ht.tsv
- config_name: en_devel-es_cr
data_files: en_devel-es_cr.tsv
- config_name: en_US-zh-rTW
data_files: en_US-zh-rTW.tsv
- config_name: en-fo
data_files: en-fo.tsv
- config_name: en-skr
data_files: en-skr.tsv
- config_name: en-ak
data_files: en-ak.tsv
- config_name: en_GB-sr@latin
data_files: en_GB-sr@latin.tsv
- config_name: en_US-de_CH
data_files: en_US-de_CH.tsv
- config_name: en_US-uk-UA
data_files: en_US-uk-UA.tsv
- config_name: en-ko_KR
data_files: en-ko_KR.tsv
- config_name: en-cy
data_files: en-cy.tsv
- config_name: en-galo
data_files: en-galo.tsv
- config_name: en-bn_BD
data_files: en-bn_BD.tsv
- config_name: en_devel-ms
data_files: en_devel-ms.tsv
- config_name: fr-it
data_files: fr-it.tsv
- config_name: en-ny
data_files: en-ny.tsv
- config_name: en-tet
data_files: en-tet.tsv
- config_name: en_GB-sk
data_files: en_GB-sk.tsv
- config_name: eo-ar
data_files: eo-ar.tsv
- config_name: eo-es
data_files: eo-es.tsv
- config_name: en-bho
data_files: en-bho.tsv
- config_name: en-pap
data_files: en-pap.tsv
- config_name: en-vi_VN
data_files: en-vi_VN.tsv
- config_name: en_US-ar
data_files: en_US-ar.tsv
- config_name: en_devel-nb
data_files: en_devel-nb.tsv
- config_name: en_devel-es_mx
data_files: en_devel-es_mx.tsv
- config_name: es-ca
data_files: es-ca.tsv
- config_name: en_GB-kn
data_files: en_GB-kn.tsv
- config_name: en-ru_UA
data_files: en-ru_UA.tsv
- config_name: sv-nb
data_files: sv-nb.tsv
- config_name: en_GB-zh_Hans
data_files: en_GB-zh_Hans.tsv
- config_name: en-he-IL
data_files: en-he-IL.tsv
- config_name: en_GB-et
data_files: en_GB-et.tsv
- config_name: es-pl
data_files: es-pl.tsv
- config_name: en-hy-AM
data_files: en-hy-AM.tsv
- config_name: en_US-cy
data_files: en_US-cy.tsv
- config_name: en-hu-rZZ
data_files: en-hu-rZZ.tsv
- config_name: en-by
data_files: en-by.tsv
- config_name: en_GB-hy
data_files: en_GB-hy.tsv
- config_name: en_US-zh-Hant
data_files: en_US-zh-Hant.tsv
- config_name: en-gu-IN
data_files: en-gu-IN.tsv
- config_name: en_GB-ml_IN
data_files: en_GB-ml_IN.tsv
- config_name: de-nl
data_files: de-nl.tsv
- config_name: en_devel-ur
data_files: en_devel-ur.tsv
- config_name: en-ca-ES
data_files: en-ca-ES.tsv
- config_name: en_GB-kl
data_files: en_GB-kl.tsv
- config_name: en_US-ta_IN
data_files: en_US-ta_IN.tsv
- config_name: en_US-sk_SK
data_files: en_US-sk_SK.tsv
- config_name: en-zh_Latn
data_files: en-zh_Latn.tsv
- config_name: en_GB-es
data_files: en_GB-es.tsv
- config_name: en-en_uk
data_files: en-en_uk.tsv
- config_name: en_GB-ru
data_files: en_GB-ru.tsv
- config_name: en-gu
data_files: en-gu.tsv
- config_name: en_US-km
data_files: en_US-km.tsv
- config_name: en_GB-uz
data_files: en_GB-uz.tsv
- config_name: en_US-yue-HK
data_files: en_US-yue-HK.tsv
- config_name: en-ceb
data_files: en-ceb.tsv
- config_name: en-is
data_files: en-is.tsv
- config_name: en-ug@Arab
data_files: en-ug@Arab.tsv
- config_name: es-ru
data_files: es-ru.tsv
- config_name: en-pt
data_files: en-pt.tsv
- config_name: en-es-US
data_files: en-es-US.tsv
- config_name: en-zh-rCMN-HANT
data_files: en-zh-rCMN-HANT.tsv
- config_name: en-jbo-EN
data_files: en-jbo-EN.tsv
- config_name: en_US-pa
data_files: en_US-pa.tsv
- config_name: en_US-or
data_files: en_US-or.tsv
- config_name: dev-hu
data_files: dev-hu.tsv
- config_name: en-b+ast
data_files: en-b+ast.tsv
- config_name: messages-vi
data_files: messages-vi.tsv
- config_name: en-ht-HT
data_files: en-ht-HT.tsv
- config_name: en-ar_AA
data_files: en-ar_AA.tsv
- config_name: en-mcc234
data_files: en-mcc234.tsv
- config_name: en_GB-he_IL
data_files: en_GB-he_IL.tsv
- config_name: en-fr_FR
data_files: en-fr_FR.tsv
- config_name: en-es_ES
data_files: en-es_ES.tsv
- config_name: en-tr-v26
data_files: en-tr-v26.tsv
- config_name: ru-kk
data_files: ru-kk.tsv
- config_name: en_GB-ky
data_files: en_GB-ky.tsv
- config_name: en-st
data_files: en-st.tsv
- config_name: en-ky
data_files: en-ky.tsv
- config_name: en_GB-fa
data_files: en_GB-fa.tsv
- config_name: en-ta
data_files: en-ta.tsv
- config_name: en_US-ru-RU
data_files: en_US-ru-RU.tsv
- config_name: en_US-it
data_files: en_US-it.tsv
- config_name: en-mai
data_files: en-mai.tsv
- config_name: en_GB-ga
data_files: en_GB-ga.tsv
- config_name: en-ay
data_files: en-ay.tsv
- config_name: en-pt_PT
data_files: en-pt_PT.tsv
- config_name: en-fa-rIR
data_files: en-fa-rIR.tsv
- config_name: en-sk_SK
data_files: en-sk_SK.tsv
- config_name: en-ru_sov
data_files: en-ru_sov.tsv
- config_name: en-pt-PT
data_files: en-pt-PT.tsv
- config_name: en_US-ko-KR
data_files: en_US-ko-KR.tsv
- config_name: en-es-rCO
data_files: en-es-rCO.tsv
- config_name: en-zh
data_files: en-zh.tsv
- config_name: en_US-ber
data_files: en_US-ber.tsv
- config_name: en-en_NZ
data_files: en-en_NZ.tsv
- config_name: eo-hi
data_files: eo-hi.tsv
- config_name: en_US-kab
data_files: en_US-kab.tsv
- config_name: en_GB-ru_RU
data_files: en_GB-ru_RU.tsv
- config_name: en-kok@latin
data_files: en-kok@latin.tsv
- config_name: en-ne_NP
data_files: en-ne_NP.tsv
- config_name: en-no-NO
data_files: en-no-NO.tsv
- config_name: it-nl_NL
data_files: it-nl_NL.tsv
- config_name: en-HE
data_files: en-HE.tsv
- config_name: eo-ja
data_files: eo-ja.tsv
- config_name: en_US-kmr
data_files: en_US-kmr.tsv
- config_name: en-pt-BR
data_files: en-pt-BR.tsv
- config_name: en-pl-v26
data_files: en-pl-v26.tsv
- config_name: en_devel-zh-tw
data_files: en_devel-zh-tw.tsv
- config_name: en-mcc235
data_files: en-mcc235.tsv
- config_name: en-el-gr
data_files: en-el-gr.tsv
- config_name: en-ga
data_files: en-ga.tsv
- config_name: en_GB-zh_CN
data_files: en_GB-zh_CN.tsv
- config_name: en_GB-kab
data_files: en_GB-kab.tsv
- config_name: en-te-IN
data_files: en-te-IN.tsv
- config_name: en_GB-de
data_files: en_GB-de.tsv
- config_name: und-de
data_files: und-de.tsv
- config_name: en-nb-rNO-v26
data_files: en-nb-rNO-v26.tsv
- config_name: en-zh_SIMPLIFIED
data_files: en-zh_SIMPLIFIED.tsv
- config_name: en-ur-rPK
data_files: en-ur-rPK.tsv
- config_name: en_US-zh-cn
data_files: en_US-zh-cn.tsv
- config_name: en_devel-pa
data_files: en_devel-pa.tsv
- config_name: en-aii
data_files: en-aii.tsv
- config_name: en_GB-it_IT
data_files: en_GB-it_IT.tsv
- config_name: en_GB-yo
data_files: en_GB-yo.tsv
- config_name: de-id
data_files: de-id.tsv
- config_name: en_GB-nv
data_files: en_GB-nv.tsv
- config_name: en-sw-KE
data_files: en-sw-KE.tsv
- config_name: en_US-so
data_files: en_US-so.tsv
- config_name: en-yue
data_files: en-yue.tsv
- config_name: en-ps
data_files: en-ps.tsv
- config_name: en-mr-IN
data_files: en-mr-IN.tsv
- config_name: de-cs
data_files: de-cs.tsv
- config_name: en_GB-pt-BR
data_files: en_GB-pt-BR.tsv
- config_name: en-ne
data_files: en-ne.tsv
- config_name: en_GB-kk
data_files: en_GB-kk.tsv
- config_name: en-af-ZA
data_files: en-af-ZA.tsv
- config_name: en-pa
data_files: en-pa.tsv
- config_name: en_US-lt
data_files: en_US-lt.tsv
- config_name: en-b+qtq+Latn
data_files: en-b+qtq+Latn.tsv
- config_name: zh_Hant-zgh
data_files: zh_Hant-zgh.tsv
- config_name: en-ta-IN
data_files: en-ta-IN.tsv
- config_name: en_GB-hu
data_files: en_GB-hu.tsv
- config_name: en-iw
data_files: en-iw.tsv
- config_name: es-hi
data_files: es-hi.tsv
- config_name: en-es_EC
data_files: en-es_EC.tsv
- config_name: en-ukrainian
data_files: en-ukrainian.tsv
- config_name: en_US-he
data_files: en_US-he.tsv
- config_name: en_GB-sl
data_files: en_GB-sl.tsv
- config_name: en_devel-sgs
data_files: en_devel-sgs.tsv
- config_name: en_US-zh-HK
data_files: en_US-zh-HK.tsv
- config_name: en_US-th_TH
data_files: en_US-th_TH.tsv
- config_name: en-nl_NL
data_files: en-nl_NL.tsv
- config_name: en-zh-HK
data_files: en-zh-HK.tsv
- config_name: en-zh-hans
data_files: en-zh-hans.tsv
- config_name: en_devel-he
data_files: en_devel-he.tsv
- config_name: en_GB-ur
data_files: en_GB-ur.tsv
- config_name: en_GB-da
data_files: en_GB-da.tsv
- config_name: en_GB-bn
data_files: en_GB-bn.tsv
- config_name: en-chinese
data_files: en-chinese.tsv
- config_name: en-bg-BG
data_files: en-bg-BG.tsv
- config_name: en_devel-jpn_JP
data_files: en_devel-jpn_JP.tsv
- config_name: en_devel-id
data_files: en_devel-id.tsv
- config_name: und-ru
data_files: und-ru.tsv
- config_name: en_devel-in
data_files: en_devel-in.tsv
- config_name: en-wo
data_files: en-wo.tsv
- config_name: nl-da
data_files: nl-da.tsv
- config_name: en-pa-Arab-PK
data_files: en-pa-Arab-PK.tsv
- config_name: en-gr-GR
data_files: en-gr-GR.tsv
- config_name: en-az-AZ
data_files: en-az-AZ.tsv
- config_name: en-bg
data_files: en-bg.tsv
- config_name: en-es-rAR
data_files: en-es-rAR.tsv
- config_name: en-nb-NO
data_files: en-nb-NO.tsv
- config_name: en_UK-bg_BG
data_files: en_UK-bg_BG.tsv
- config_name: en_GB-pap
data_files: en_GB-pap.tsv
- config_name: en_US-es
data_files: en_US-es.tsv
- config_name: en_US-hu
data_files: en_US-hu.tsv
- config_name: en-or-IN
data_files: en-or-IN.tsv
- config_name: en-guw
data_files: en-guw.tsv
- config_name: en-nl-BE
data_files: en-nl-BE.tsv
- config_name: en-ml-rIN
data_files: en-ml-rIN.tsv
- config_name: en-ji
data_files: en-ji.tsv
- config_name: en_US-ta
data_files: en_US-ta.tsv
- config_name: es-ur
data_files: es-ur.tsv
- config_name: en-br
data_files: en-br.tsv
- config_name: de-en
data_files: de-en.tsv
- config_name: dev-fr
data_files: dev-fr.tsv
- config_name: en-ace
data_files: en-ace.tsv
- config_name: en_US-zh_TW
data_files: en_US-zh_TW.tsv
- config_name: en-oj
data_files: en-oj.tsv
- config_name: en-zh_tw
data_files: en-zh_tw.tsv
- config_name: en-cnr
data_files: en-cnr.tsv
- config_name: en_devel-es_hn
data_files: en_devel-es_hn.tsv
- config_name: dev-uk
data_files: dev-uk.tsv
- config_name: en-ru_CARES
data_files: en-ru_CARES.tsv
- config_name: en-uroc
data_files: en-uroc.tsv
- config_name: en_GB-bg_BG
data_files: en_GB-bg_BG.tsv
- config_name: en_GB-ar_SA
data_files: en_GB-ar_SA.tsv
- config_name: en_US-fy
data_files: en_US-fy.tsv
- config_name: en-lt
data_files: en-lt.tsv
- config_name: en-de-rDE
data_files: en-de-rDE.tsv
- config_name: en_US-ast
data_files: en_US-ast.tsv
- config_name: en_US-ko_KR
data_files: en_US-ko_KR.tsv
- config_name: en_devel-ar_DZ
data_files: en_devel-ar_DZ.tsv
- config_name: en_devel-hu
data_files: en_devel-hu.tsv
- config_name: en-fr_BE
data_files: en-fr_BE.tsv
- config_name: en-kmr
data_files: en-kmr.tsv
- config_name: en_devel-ro_ro
data_files: en_devel-ro_ro.tsv
- config_name: en_GB-vi_VN
data_files: en_GB-vi_VN.tsv
- config_name: en_devel-sk
data_files: en_devel-sk.tsv
- config_name: und-nl_BE
data_files: und-nl_BE.tsv
- config_name: eo-bn
data_files: eo-bn.tsv
- config_name: en-hungarian
data_files: en-hungarian.tsv
- config_name: en_GB-ta
data_files: en_GB-ta.tsv
- config_name: en_US-ca
data_files: en_US-ca.tsv
- config_name: en-oc
data_files: en-oc.tsv
- config_name: en_US-bg_BG
data_files: en_US-bg_BG.tsv
- config_name: en-hr
data_files: en-hr.tsv
- config_name: en_GB-zh_Hant
data_files: en_GB-zh_Hant.tsv
- config_name: en_GB-bn_BD
data_files: en_GB-bn_BD.tsv
- config_name: en-ca@valencia
data_files: en-ca@valencia.tsv
- config_name: en_GB-mai
data_files: en_GB-mai.tsv
- config_name: en-uk-UA
data_files: en-uk-UA.tsv
- config_name: en-frm
data_files: en-frm.tsv
- config_name: en-bd
data_files: en-bd.tsv
- config_name: en_GB-ja
data_files: en_GB-ja.tsv
- config_name: en_US-sw
data_files: en_US-sw.tsv
- config_name: eo-uk
data_files: eo-uk.tsv
- config_name: en_US-es-rAR
data_files: en_US-es-rAR.tsv
- config_name: en-az-rAZ
data_files: en-az-rAZ.tsv
- config_name: en_GB-es-ES
data_files: en_GB-es-ES.tsv
- config_name: en-sl-SL
data_files: en-sl-SL.tsv
- config_name: en-pms
data_files: en-pms.tsv
- config_name: en_GB-te
data_files: en_GB-te.tsv
- config_name: it-de_DE
data_files: it-de_DE.tsv
- config_name: en-yue_Hant
data_files: en-yue_Hant.tsv
- config_name: en-en-rIN
data_files: en-en-rIN.tsv
- config_name: en-ln
data_files: en-ln.tsv
- config_name: en-pt-rBR
data_files: en-pt-rBR.tsv
- config_name: en_US-az_AZ
data_files: en_US-az_AZ.tsv
- config_name: en-pl-rPL
data_files: en-pl-rPL.tsv
- config_name: eo-el
data_files: eo-el.tsv
- config_name: eo-ms
data_files: eo-ms.tsv
- config_name: en_US-tr
data_files: en_US-tr.tsv
- config_name: en-en_SHAW
data_files: en-en_SHAW.tsv
- config_name: en-ar-rIQ
data_files: en-ar-rIQ.tsv
- config_name: en-yo
data_files: en-yo.tsv
- config_name: en-japanese
data_files: en-japanese.tsv
- config_name: es-id
data_files: es-id.tsv
- config_name: en-fa_AF
data_files: en-fa_AF.tsv
- config_name: en_GB-ms
data_files: en_GB-ms.tsv
- config_name: en-Zh-CHS
data_files: en-Zh-CHS.tsv
- config_name: en_GB-mt
data_files: en_GB-mt.tsv
- config_name: en-b+de
data_files: en-b+de.tsv
- config_name: en_US-fi
data_files: en_US-fi.tsv
- config_name: de-ar
data_files: de-ar.tsv
- config_name: en-en-GB
data_files: en-en-GB.tsv
- config_name: en-mo
data_files: en-mo.tsv
- config_name: en_devel-zh_Hans
data_files: en_devel-zh_Hans.tsv
- config_name: en_GB-dz
data_files: en_GB-dz.tsv
- config_name: en_US-gl
data_files: en_US-gl.tsv
- config_name: en-pt-rPT
data_files: en-pt-rPT.tsv
- config_name: en_devel-es_pr
data_files: en_devel-es_pr.tsv
- config_name: en-RU
data_files: en-RU.tsv
- config_name: en-en-rUS
data_files: en-en-rUS.tsv
- config_name: en-sv_se
data_files: en-sv_se.tsv
- config_name: en-italian
data_files: en-italian.tsv
- config_name: en_US-lv
data_files: en_US-lv.tsv
- config_name: de-ru
data_files: de-ru.tsv
- config_name: en-sc
data_files: en-sc.tsv
- config_name: en-gv
data_files: en-gv.tsv
- config_name: en_US-pt_PT
data_files: en_US-pt_PT.tsv
- config_name: en_GB-bn_IN
data_files: en_GB-bn_IN.tsv
- config_name: en_US-fr-FR
data_files: en_US-fr-FR.tsv
- config_name: ia-es
data_files: ia-es.tsv
- config_name: en_US-es_UY
data_files: en_US-es_UY.tsv
- config_name: en_GB-hr_HR
data_files: en_GB-hr_HR.tsv
- config_name: en-id_ID
data_files: en-id_ID.tsv
- config_name: en-es_VE
data_files: en-es_VE.tsv
- config_name: en-ie
data_files: en-ie.tsv
- config_name: en-it_IT
data_files: en-it_IT.tsv
- config_name: en_GB-si_LK
data_files: en_GB-si_LK.tsv
- config_name: en-nqo
data_files: en-nqo.tsv
- config_name: pl-uk
data_files: pl-uk.tsv
- config_name: en-sco
data_files: en-sco.tsv
- config_name: en_US-tr-TR
data_files: en_US-tr-TR.tsv
- config_name: en-en_GB
data_files: en-en_GB.tsv
- config_name: en-b+kab
data_files: en-b+kab.tsv
- config_name: en-he-rIL
data_files: en-he-rIL.tsv
- config_name: en-pu
data_files: en-pu.tsv
- config_name: de-lb
data_files: de-lb.tsv
- config_name: en-is_IS
data_files: en-is_IS.tsv
- config_name: en_US-cs
data_files: en_US-cs.tsv
- config_name: en_GB-nah
data_files: en_GB-nah.tsv
- config_name: de-tr
data_files: de-tr.tsv
- config_name: zh_Hant-en_US
data_files: zh_Hant-en_US.tsv
- config_name: pl-ru
data_files: pl-ru.tsv
- config_name: en-zh-TW
data_files: en-zh-TW.tsv
- config_name: en_GB-kok
data_files: en_GB-kok.tsv
- config_name: en_US-zh-Hans
data_files: en_US-zh-Hans.tsv
- config_name: en_devel-da
data_files: en_devel-da.tsv
- config_name: en-mg
data_files: en-mg.tsv
- config_name: en-pa-rIN
data_files: en-pa-rIN.tsv
- config_name: en-nb_NO
data_files: en-nb_NO.tsv
- config_name: en_GB-az
data_files: en_GB-az.tsv
- config_name: en-ca_valencia
data_files: en-ca_valencia.tsv
- config_name: en-su
data_files: en-su.tsv
- config_name: und-sv
data_files: und-sv.tsv
- config_name: pl-en
data_files: pl-en.tsv
- config_name: en-ar-rDZ
data_files: en-ar-rDZ.tsv
- config_name: en_US-eo
data_files: en_US-eo.tsv
- config_name: en_US-sq
data_files: en_US-sq.tsv
- config_name: en-sl-rSI
data_files: en-sl-rSI.tsv
- config_name: en-uk-rUA
data_files: en-uk-rUA.tsv
- config_name: en_devel-te
data_files: en_devel-te.tsv
- config_name: en-da_DK
data_files: en-da_DK.tsv
- config_name: en_GB-et_EE
data_files: en_GB-et_EE.tsv
- config_name: en-et-EE
data_files: en-et-EE.tsv
- config_name: en-pa_IN
data_files: en-pa_IN.tsv
- config_name: en_US-nn
data_files: en_US-nn.tsv
- config_name: en_GB-xh
data_files: en_GB-xh.tsv
- config_name: en_devel-sv
data_files: en_devel-sv.tsv
- config_name: en-ru-rRU
data_files: en-ru-rRU.tsv
- config_name: en_US-hr
data_files: en_US-hr.tsv
- config_name: en-sr_Latn
data_files: en-sr_Latn.tsv
- config_name: en_GB-uk
data_files: en_GB-uk.tsv
- config_name: en_GB-ee
data_files: en_GB-ee.tsv
- config_name: en_devel-ta
data_files: en_devel-ta.tsv
- config_name: en_US-hu-HU
data_files: en_US-hu-HU.tsv
- config_name: en_GB-ak
data_files: en_GB-ak.tsv
- config_name: en_US-ia
data_files: en_US-ia.tsv
- config_name: en_UK-it_IT
data_files: en_UK-it_IT.tsv
- config_name: en-ru
data_files: en-ru.tsv
- config_name: en_US-es-ar
data_files: en_US-es-ar.tsv
- config_name: en_US-lo
data_files: en_US-lo.tsv
- config_name: en-ur-PK
data_files: en-ur-PK.tsv
- config_name: en_devel-nb_NO
data_files: en_devel-nb_NO.tsv
- config_name: en_GB-es_ES
data_files: en_GB-es_ES.tsv
- config_name: en_GB-ast
data_files: en_GB-ast.tsv
- config_name: en-hr-HR
data_files: en-hr-HR.tsv
- config_name: en-fr@informal
data_files: en-fr@informal.tsv
- config_name: en-es_ar
data_files: en-es_ar.tsv
- config_name: en-ms_MY
data_files: en-ms_MY.tsv
- config_name: en-el_GR
data_files: en-el_GR.tsv
- config_name: en_devel-ka
data_files: en_devel-ka.tsv
- config_name: en-fr-FR
data_files: en-fr-FR.tsv
- config_name: en_US-kk
data_files: en_US-kk.tsv
- config_name: es-ko
data_files: es-ko.tsv
- config_name: en-fr_AG
data_files: en-fr_AG.tsv
- config_name: en-zh-tw
data_files: en-zh-tw.tsv
- config_name: en-BrazilianPortuguese
data_files: en-BrazilianPortuguese.tsv
- config_name: en_GB-am
data_files: en_GB-am.tsv
- config_name: en-tam
data_files: en-tam.tsv
- config_name: en_US-af
data_files: en_US-af.tsv
- config_name: en_US-is
data_files: en_US-is.tsv
- config_name: en_GB-en_US
data_files: en_GB-en_US.tsv
- config_name: en-az
data_files: en-az.tsv
- config_name: en-en@pirate
data_files: en-en@pirate.tsv
- config_name: en_GB-fil
data_files: en_GB-fil.tsv
- config_name: en_US-pl_PL
data_files: en_US-pl_PL.tsv
- config_name: en_US-sl
data_files: en_US-sl.tsv
- config_name: en_US-nl
data_files: en_US-nl.tsv
- config_name: es-it
data_files: es-it.tsv
- config_name: en_GB-bar
data_files: en_GB-bar.tsv
- config_name: it-nb_NO
data_files: it-nb_NO.tsv
- config_name: eo-it
data_files: eo-it.tsv
- config_name: en_US-yue
data_files: en_US-yue.tsv
- config_name: en-glk
data_files: en-glk.tsv
- config_name: en-fi_FI
data_files: en-fi_FI.tsv
- config_name: es-cs
data_files: es-cs.tsv
- config_name: en_GB-pt_BR
data_files: en_GB-pt_BR.tsv
- config_name: en_GB-zgh
data_files: en_GB-zgh.tsv
- config_name: en_US-nl-BE
data_files: en_US-nl-BE.tsv
- config_name: en-ru-rCH
data_files: en-ru-rCH.tsv
- config_name: en-sr_CS
data_files: en-sr_CS.tsv
- config_name: en-ur
data_files: en-ur.tsv
- config_name: en_GB-th
data_files: en_GB-th.tsv
- config_name: en_US-id_ID
data_files: en_US-id_ID.tsv
- config_name: en_US-be_BY
data_files: en_US-be_BY.tsv
- config_name: en_devel-es_us
data_files: en_devel-es_us.tsv
- config_name: en-fr_CA
data_files: en-fr_CA.tsv
- config_name: en_GB-en
data_files: en_GB-en.tsv
- config_name: en_US-sk
data_files: en_US-sk.tsv
- config_name: en-uz-Latn
data_files: en-uz-Latn.tsv
- config_name: en_devel-eu
data_files: en_devel-eu.tsv
- config_name: en_GB-is_IS
data_files: en_GB-is_IS.tsv
- config_name: sl-en
data_files: sl-en.tsv
- config_name: en-ja_JA
data_files: en-ja_JA.tsv
- config_name: en-bn-BD
data_files: en-bn-BD.tsv
- config_name: fr-de
data_files: fr-de.tsv
- config_name: en-sr_SP
data_files: en-sr_SP.tsv
- config_name: en-nb-no
data_files: en-nb-no.tsv
- config_name: fr-nb_NO
data_files: fr-nb_NO.tsv
- config_name: en_US-lb
data_files: en_US-lb.tsv
- config_name: en-zh_hant
data_files: en-zh_hant.tsv
- config_name: en-be
data_files: en-be.tsv
- config_name: en_US-si
data_files: en_US-si.tsv
- config_name: en-ltg
data_files: en-ltg.tsv
- config_name: en-es_cl
data_files: en-es_cl.tsv
- config_name: en_US-gu
data_files: en_US-gu.tsv
- config_name: en-lb_LU
data_files: en-lb_LU.tsv
- config_name: en-ain
data_files: en-ain.tsv
- config_name: en-de
data_files: en-de.tsv
- config_name: en-es
data_files: en-es.tsv
- config_name: en-belarusian
data_files: en-belarusian.tsv
- config_name: en-kok
data_files: en-kok.tsv
- config_name: nl-fr
data_files: nl-fr.tsv
- config_name: en-ar_SA
data_files: en-ar_SA.tsv
- config_name: en-tk
data_files: en-tk.tsv
- config_name: en-kab
data_files: en-kab.tsv
- config_name: en-or-rIN
data_files: en-or-rIN.tsv
- config_name: en-ja-KS
data_files: en-ja-KS.tsv
- config_name: en-en-Shaw
data_files: en-en-Shaw.tsv
- config_name: en_GB-lo
data_files: en_GB-lo.tsv
- config_name: en_GB-gl_ES
data_files: en_GB-gl_ES.tsv
- config_name: en-sd
data_files: en-sd.tsv
- config_name: en_devel-es_ar
data_files: en_devel-es_ar.tsv
- config_name: en-he-il
data_files: en-he-il.tsv
- config_name: en_GB-zh_TW
data_files: en_GB-zh_TW.tsv
- config_name: en-cs_cz
data_files: en-cs_cz.tsv
- config_name: en_GB-mn
data_files: en_GB-mn.tsv
- config_name: en_US-jv
data_files: en_US-jv.tsv
- config_name: eo-nl
data_files: eo-nl.tsv
- config_name: en-zh_cn
data_files: en-zh_cn.tsv
- config_name: en-he_IL
data_files: en-he_IL.tsv
- config_name: en-IT
data_files: en-IT.tsv
- config_name: en-ja
data_files: en-ja.tsv
- config_name: en_US-fr-ca
data_files: en_US-fr-ca.tsv
- config_name: en-bqi
data_files: en-bqi.tsv
- config_name: en-ro-rRO
data_files: en-ro-rRO.tsv
- config_name: en-krl
data_files: en-krl.tsv
- config_name: en_US-tr_TR
data_files: en_US-tr_TR.tsv
- config_name: pl-lt
data_files: pl-lt.tsv
- config_name: en-zh_Hant_HK
data_files: en-zh_Hant_HK.tsv
- config_name: en_GB-sv_SE
data_files: en_GB-sv_SE.tsv
- config_name: en_US-pt-br
data_files: en_US-pt-br.tsv
- config_name: en-id-ID
data_files: en-id-ID.tsv
- config_name: en-fu
data_files: en-fu.tsv
- config_name: en-French
data_files: en-French.tsv
- config_name: eo-zh
data_files: eo-zh.tsv
- config_name: en-v20
data_files: en-v20.tsv
- config_name: en-iw-IL
data_files: en-iw-IL.tsv
- config_name: en_GB-af
data_files: en_GB-af.tsv
- config_name: en_GB-el
data_files: en_GB-el.tsv
- config_name: en-pa-IN
data_files: en-pa-IN.tsv
- config_name: en_devel-es_ve
data_files: en_devel-es_ve.tsv
- config_name: und-nb_NO
data_files: und-nb_NO.tsv
- config_name: en-lo
data_files: en-lo.tsv
- config_name: en-ar
data_files: en-ar.tsv
- config_name: en-b+zh+HANS+CN
data_files: en-b+zh+HANS+CN.tsv
- config_name: en_GB-byn
data_files: en_GB-byn.tsv
- config_name: en-en-rXC
data_files: en-en-rXC.tsv
- config_name: zh_Hant-nb_NO
data_files: zh_Hant-nb_NO.tsv
- config_name: en-fr
data_files: en-fr.tsv
- config_name: en-zh_HANT
data_files: en-zh_HANT.tsv
- config_name: en_US-fa-IR
data_files: en_US-fa-IR.tsv
- config_name: en_GB-vi
data_files: en_GB-vi.tsv
- config_name: en-Spanish
data_files: en-Spanish.tsv
- config_name: en-am_ET
data_files: en-am_ET.tsv
- config_name: en_devel-bn
data_files: en_devel-bn.tsv
- config_name: en-zh-cn
data_files: en-zh-cn.tsv
- config_name: en-tr-rTR
data_files: en-tr-rTR.tsv
- config_name: fr-cs
data_files: fr-cs.tsv
- config_name: en_US-nl-rBE
data_files: en_US-nl-rBE.tsv
- config_name: es-en
data_files: es-en.tsv
- config_name: en-sr@Cyrl
data_files: en-sr@Cyrl.tsv
- config_name: fr-eu
data_files: fr-eu.tsv
- config_name: en_US-pl
data_files: en_US-pl.tsv
- config_name: en_US-nan
data_files: en_US-nan.tsv
- config_name: en_devel-pt-rBR
data_files: en_devel-pt-rBR.tsv
- config_name: en-sr_lat
data_files: en-sr_lat.tsv
- config_name: en_devel-no
data_files: en_devel-no.tsv
- config_name: pl-de
data_files: pl-de.tsv
- config_name: en-tlh
data_files: en-tlh.tsv
- config_name: en_US-cs_CZ
data_files: en_US-cs_CZ.tsv
- config_name: eo-pl
data_files: eo-pl.tsv
- config_name: en_devel-gl
data_files: en_devel-gl.tsv
- config_name: en-fi-FI
data_files: en-fi-FI.tsv
- config_name: en_US-ca_CA
data_files: en_US-ca_CA.tsv
- config_name: en_US-nb
data_files: en_US-nb.tsv
- config_name: en-is-IS
data_files: en-is-IS.tsv
- config_name: en_GB-io
data_files: en_GB-io.tsv
- config_name: en-UK
data_files: en-UK.tsv
- config_name: en-pt-pt
data_files: en-pt-pt.tsv
- config_name: en-fil
data_files: en-fil.tsv
- config_name: en-mi
data_files: en-mi.tsv
- config_name: en-sr-Cyrl
data_files: en-sr-Cyrl.tsv
- config_name: en_devel-hi
data_files: en_devel-hi.tsv
- config_name: en-nb-NB
data_files: en-nb-NB.tsv
- config_name: en-mnc
data_files: en-mnc.tsv
- config_name: en-mk
data_files: en-mk.tsv
- config_name: en-hrx
data_files: en-hrx.tsv
- config_name: en-ar_MA
data_files: en-ar_MA.tsv
- config_name: en_devel-es
data_files: en_devel-es.tsv
- config_name: en_GB-zh-rCN
data_files: en_GB-zh-rCN.tsv
- config_name: en-sa
data_files: en-sa.tsv
- config_name: en-bs
data_files: en-bs.tsv
- config_name: en_GB-tg
data_files: en_GB-tg.tsv
- config_name: en-si-LK
data_files: en-si-LK.tsv
- config_name: en-lt-LT
data_files: en-lt-LT.tsv
- config_name: en-hi
data_files: en-hi.tsv
- config_name: en-hu_hu
data_files: en-hu_hu.tsv
- config_name: en-mk_MK
data_files: en-mk_MK.tsv
- config_name: en_GB-de_DE
data_files: en_GB-de_DE.tsv
- config_name: messages-eo
data_files: messages-eo.tsv
- config_name: en-ku_IQ
data_files: en-ku_IQ.tsv
- config_name: en-rcf
data_files: en-rcf.tsv
- config_name: en-uz
data_files: en-uz.tsv
- config_name: en-by_lat
data_files: en-by_lat.tsv
- config_name: ia-nb_NO
data_files: ia-nb_NO.tsv
- config_name: messages-ko
data_files: messages-ko.tsv
- config_name: en_US-pt-rBR
data_files: en_US-pt-rBR.tsv
- config_name: en_GB-zu
data_files: en_GB-zu.tsv
- config_name: es-hr
data_files: es-hr.tsv
- config_name: en_devel-th
data_files: en_devel-th.tsv
- config_name: en-af
data_files: en-af.tsv
- config_name: en-ms-MY
data_files: en-ms-MY.tsv
- config_name: en-sr-Latn-RS
data_files: en-sr-Latn-RS.tsv
- config_name: en-de-ZH
data_files: en-de-ZH.tsv
- config_name: en-b+sr+Latn
data_files: en-b+sr+Latn.tsv
- config_name: en-cn
data_files: en-cn.tsv
- config_name: de-zh_Hans
data_files: de-zh_Hans.tsv
- config_name: en_devel-gu
data_files: en_devel-gu.tsv
- config_name: en_US-et_EE
data_files: en_US-et_EE.tsv
- config_name: en-und
data_files: en-und.tsv
- config_name: en_devel-es_ni
data_files: en_devel-es_ni.tsv
- config_name: en-en-rNZ
data_files: en-en-rNZ.tsv
- config_name: pl-fr
data_files: pl-fr.tsv
- config_name: de-es
data_files: de-es.tsv
- config_name: en-pt_br
data_files: en-pt_br.tsv
- config_name: en-gug
data_files: en-gug.tsv
- config_name: fr-fr
data_files: fr-fr.tsv
- config_name: en-fr-rFR
data_files: en-fr-rFR.tsv
- config_name: en-dsb
data_files: en-dsb.tsv
- config_name: en-tr-TR
data_files: en-tr-TR.tsv
- config_name: en-tw
data_files: en-tw.tsv
- config_name: en-bs_Latn
data_files: en-bs_Latn.tsv
- config_name: en_GB-hi
data_files: en_GB-hi.tsv
- config_name: en-norwegian
data_files: en-norwegian.tsv
- config_name: en-zh_Latn_pinyin
data_files: en-zh_Latn_pinyin.tsv
- config_name: en_US-es-mx
data_files: en_US-es-mx.tsv
- config_name: en_GB-nl_NL
data_files: en_GB-nl_NL.tsv
- config_name: es-bn
data_files: es-bn.tsv
- config_name: en-peo
data_files: en-peo.tsv
- config_name: en-de_LU
data_files: en-de_LU.tsv
- config_name: en-mni
data_files: en-mni.tsv
- config_name: en_GB-jam
data_files: en_GB-jam.tsv
- config_name: en-sr_cyr
data_files: en-sr_cyr.tsv
- config_name: en-ro-RO
data_files: en-ro-RO.tsv
- config_name: en-doi
data_files: en-doi.tsv
- config_name: en_GB-en-US
data_files: en_GB-en-US.tsv
- config_name: en-he
data_files: en-he.tsv
- config_name: en-et
data_files: en-et.tsv
- config_name: en-tl_PH
data_files: en-tl_PH.tsv
- config_name: en-sr-Cyrl-RS
data_files: en-sr-Cyrl-RS.tsv
- config_name: en-Dutch
data_files: en-Dutch.tsv
- config_name: en-uz_UZ
data_files: en-uz_UZ.tsv
- config_name: en-ur-rIN
data_files: en-ur-rIN.tsv
- config_name: en-kn
data_files: en-kn.tsv
- config_name: en-trv
data_files: en-trv.tsv
- config_name: en_US-ms_MY
data_files: en_US-ms_MY.tsv
- config_name: en-de-rFO
data_files: en-de-rFO.tsv
- config_name: en-zh-CN
data_files: en-zh-CN.tsv
- config_name: ru-de
data_files: ru-de.tsv
- config_name: en-pt_BR
data_files: en-pt_BR.tsv
- config_name: en_GB-ms_MY
data_files: en_GB-ms_MY.tsv
- config_name: en_GB-tr
data_files: en_GB-tr.tsv
- config_name: en-bn_IN
data_files: en-bn_IN.tsv
- config_name: en_GB-pt
data_files: en_GB-pt.tsv
- config_name: en_GB-wa
data_files: en_GB-wa.tsv
- config_name: en_US-te
data_files: en_US-te.tsv
- config_name: en-da-rDK
data_files: en-da-rDK.tsv
- config_name: en_US-zh_CN
data_files: en_US-zh_CN.tsv
- config_name: en_US-az
data_files: en_US-az.tsv
- config_name: en-sn
data_files: en-sn.tsv
- config_name: en_devel-zh_Hant
data_files: en_devel-zh_Hant.tsv
- config_name: en-sw
data_files: en-sw.tsv
- config_name: en-fr_fr
data_files: en-fr_fr.tsv
- config_name: en_GB-mhr
data_files: en_GB-mhr.tsv
- config_name: sv-se
data_files: sv-se.tsv
- config_name: en-mn
data_files: en-mn.tsv
- config_name: en-gl
data_files: en-gl.tsv
- config_name: en_GB-is
data_files: en_GB-is.tsv
- config_name: en-nl-NL
data_files: en-nl-NL.tsv
- config_name: dev-fa
data_files: dev-fa.tsv
- config_name: en-frp
data_files: en-frp.tsv
- config_name: en_GB-it
data_files: en_GB-it.tsv
- config_name: en_US-ja-JP
data_files: en_US-ja-JP.tsv
- config_name: en_US-vi_VN
data_files: en_US-vi_VN.tsv
- config_name: en-zu
data_files: en-zu.tsv
- config_name: en_US-zh_HK
data_files: en_US-zh_HK.tsv
- config_name: en_UK-nb_NO
data_files: en_UK-nb_NO.tsv
- config_name: en_GB-eo
data_files: en_GB-eo.tsv
- config_name: en-ar_YE
data_files: en-ar_YE.tsv
- config_name: messages-pt
data_files: messages-pt.tsv
- config_name: en_devel-hr
data_files: en_devel-hr.tsv
- config_name: ia-en
data_files: ia-en.tsv
- config_name: en-sr
data_files: en-sr.tsv
- config_name: en_US-el_GR
data_files: en_US-el_GR.tsv
- config_name: en_US-bg
data_files: en_US-bg.tsv
- config_name: en-be@latin
data_files: en-be@latin.tsv
- config_name: en_US-zh_Hant
data_files: en_US-zh_Hant.tsv
- config_name: eo-fr
data_files: eo-fr.tsv
- config_name: en-uk_UA
data_files: en-uk_UA.tsv
- config_name: en_US-pt-BR
data_files: en_US-pt-BR.tsv
- config_name: nl-ko
data_files: nl-ko.tsv
- config_name: en-sl-SI
data_files: en-sl-SI.tsv
- config_name: en-to
data_files: en-to.tsv
- config_name: en_GB-ne
data_files: en_GB-ne.tsv
- config_name: en-la
data_files: en-la.tsv
- config_name: ru-ua
data_files: ru-ua.tsv
- config_name: en_GB-ia
data_files: en_GB-ia.tsv
- config_name: en_US-bn_BD
data_files: en_US-bn_BD.tsv
- config_name: en-zh_Hant
data_files: en-zh_Hant.tsv
- config_name: en_devel-nl_BE
data_files: en_devel-nl_BE.tsv
- config_name: en-id
data_files: en-id.tsv
- config_name: en_GB-pa
data_files: en_GB-pa.tsv
- config_name: en-gl_ES
data_files: en-gl_ES.tsv
- config_name: en-vi
data_files: en-vi.tsv
- config_name: fr-es
data_files: fr-es.tsv
- config_name: en-udm
data_files: en-udm.tsv
- config_name: en-es-rUS
data_files: en-es-rUS.tsv
- config_name: en-b+tok
data_files: en-b+tok.tsv
- config_name: it-fr_FR
data_files: it-fr_FR.tsv
- config_name: und-nl
data_files: und-nl.tsv
- config_name: en-pt_pt
data_files: en-pt_pt.tsv
- config_name: en-es_419
data_files: en-es_419.tsv
- config_name: en-jbo
data_files: en-jbo.tsv
- config_name: en_GB-nb-rNO
data_files: en_GB-nb-rNO.tsv
- config_name: en_GB-nl
data_files: en_GB-nl.tsv
- config_name: en-gl-ES
data_files: en-gl-ES.tsv
- config_name: en-de_AT
data_files: en-de_AT.tsv
- config_name: en-mk-MK
data_files: en-mk-MK.tsv
- config_name: en_GB-bg
data_files: en_GB-bg.tsv
- config_name: en_US-sc
data_files: en_US-sc.tsv
- config_name: en_US-kn
data_files: en_US-kn.tsv
- config_name: en-cy_GB
data_files: en-cy_GB.tsv
- config_name: en_US-mn
data_files: en_US-mn.tsv
- config_name: de-uk
data_files: de-uk.tsv
- config_name: en_GB-ko
data_files: en_GB-ko.tsv
- config_name: en-nl-rNL
data_files: en-nl-rNL.tsv
- config_name: en_devel-pt_PT
data_files: en_devel-pt_PT.tsv
- config_name: en_US-fi_FI
data_files: en_US-fi_FI.tsv
- config_name: en_devel-vi
data_files: en_devel-vi.tsv
- config_name: en_US-ru
data_files: en_US-ru.tsv
- config_name: en-hne
data_files: en-hne.tsv
- config_name: en-fi
data_files: en-fi.tsv
- config_name: en-ru_RU
data_files: en-ru_RU.tsv
- config_name: en_devel-es_cl
data_files: en_devel-es_cl.tsv
- config_name: de-el
data_files: de-el.tsv
- config_name: en_devel-ro
data_files: en_devel-ro.tsv
- config_name: en_GB-tt
data_files: en_GB-tt.tsv
- config_name: en-eng_GB
data_files: en-eng_GB.tsv
- config_name: en-lt-rLT
data_files: en-lt-rLT.tsv
- config_name: en-ota
data_files: en-ota.tsv
- config_name: en_devel-es_co
data_files: en_devel-es_co.tsv
- config_name: en-russian
data_files: en-russian.tsv
- config_name: en-ar-MA
data_files: en-ar-MA.tsv
- config_name: en-nn
data_files: en-nn.tsv
- config_name: eo-en
data_files: eo-en.tsv
- config_name: en_GB-cv
data_files: en_GB-cv.tsv
- config_name: en_devel-id_ID
data_files: en_devel-id_ID.tsv
- config_name: en_US-nb-NO
data_files: en_US-nb-NO.tsv
- config_name: en-it-rIT
data_files: en-it-rIT.tsv
- config_name: en_US-pl-PL
data_files: en_US-pl-PL.tsv
- config_name: en-ext
data_files: en-ext.tsv
- config_name: en-ko
data_files: en-ko.tsv
- config_name: en-tg
data_files: en-tg.tsv
- config_name: en-ga_IE
data_files: en-ga_IE.tsv
- config_name: en_devel-sr
data_files: en_devel-sr.tsv
- config_name: en-PT
data_files: en-PT.tsv
- config_name: en-sv
data_files: en-sv.tsv
- config_name: en_GB-son
data_files: en_GB-son.tsv
- config_name: en-et_ee
data_files: en-et_ee.tsv
- config_name: en_GB-el_GR
data_files: en_GB-el_GR.tsv
- config_name: en-jp
data_files: en-jp.tsv
- config_name: en-ga-rIE
data_files: en-ga-rIE.tsv
- config_name: sv-en
data_files: sv-en.tsv
- config_name: en_US-ua
data_files: en_US-ua.tsv
- config_name: en-sm
data_files: en-sm.tsv
- config_name: en-nap
data_files: en-nap.tsv
- config_name: en-portuguese
data_files: en-portuguese.tsv
- config_name: en_US-nl-NL
data_files: en_US-nl-NL.tsv
- config_name: en-es_ec
data_files: en-es_ec.tsv
- config_name: en_GB-crh
data_files: en_GB-crh.tsv
- config_name: en-tr_TR
data_files: en-tr_TR.tsv
- config_name: en-sr_RS@latin
data_files: en-sr_RS@latin.tsv
- config_name: en-bg_BG
data_files: en-bg_BG.tsv
- config_name: en-hu
data_files: en-hu.tsv
- config_name: en-es_SV
data_files: en-es_SV.tsv
- config_name: en_GB-rw
data_files: en_GB-rw.tsv
- config_name: en-es_AR
data_files: en-es_AR.tsv
- config_name: en_devel-es_pe
data_files: en_devel-es_pe.tsv
- config_name: en-et-rEE
data_files: en-et-rEE.tsv
- config_name: en-ro-v26
data_files: en-ro-v26.tsv
- config_name: en-ne-NP
data_files: en-ne-NP.tsv
- config_name: en-es-ar
data_files: en-es-ar.tsv
- config_name: en-en_ZA
data_files: en-en_ZA.tsv
- config_name: en_devel-lt
data_files: en_devel-lt.tsv
- config_name: en-eg
data_files: en-eg.tsv
- config_name: zh_Latn-zh_Hans
data_files: zh_Latn-zh_Hans.tsv
- config_name: en_GB-so
data_files: en_GB-so.tsv
- config_name: en-hr-rHR
data_files: en-hr-rHR.tsv
- config_name: en-lt_LT
data_files: en-lt_LT.tsv
- config_name: en-io
data_files: en-io.tsv
- config_name: en-sh-rHR
data_files: en-sh-rHR.tsv
- config_name: en-uk
data_files: en-uk.tsv
- config_name: en_GB-cs-CZ
data_files: en_GB-cs-CZ.tsv
- config_name: en-de-rCH
data_files: en-de-rCH.tsv
- config_name: en-nah
data_files: en-nah.tsv
- config_name: en_devel-tr
data_files: en_devel-tr.tsv
- config_name: en-de-rAT
data_files: en-de-rAT.tsv
- config_name: eo-sv
data_files: eo-sv.tsv
- config_name: en-nb
data_files: en-nb.tsv
- config_name: en_GB-ab
data_files: en_GB-ab.tsv
- config_name: en_US-de-DE
data_files: en_US-de-DE.tsv
- config_name: en-de_alm_x
data_files: en-de_alm_x.tsv
- config_name: en_GB-it-IT
data_files: en_GB-it-IT.tsv
- config_name: en-aa
data_files: en-aa.tsv
- config_name: en_devel-sq
data_files: en_devel-sq.tsv
- config_name: en_devel-en_au
data_files: en_devel-en_au.tsv
- config_name: en-sl
data_files: en-sl.tsv
- config_name: en-sr-rSP
data_files: en-sr-rSP.tsv
- config_name: en-ckb
data_files: en-ckb.tsv
- config_name: en_devel-pt_pt
data_files: en_devel-pt_pt.tsv
- config_name: en_devel-ar
data_files: en_devel-ar.tsv
- config_name: en-nn-NO
data_files: en-nn-NO.tsv
- config_name: es-fr
data_files: es-fr.tsv
- config_name: en-mk-rMK
data_files: en-mk-rMK.tsv
- config_name: en-spanish
data_files: en-spanish.tsv
- config_name: en_GB-ve
data_files: en_GB-ve.tsv
- config_name: en_GB-zh_HK
data_files: en_GB-zh_HK.tsv
- config_name: en_GB-kmr
data_files: en_GB-kmr.tsv
- config_name: en-no_nb
data_files: en-no_nb.tsv
- config_name: en_GB-sq
data_files: en_GB-sq.tsv
- config_name: en_US-ro-RO
data_files: en_US-ro-RO.tsv
- config_name: en-zh-rHK
data_files: en-zh-rHK.tsv
- config_name: en-Russian
data_files: en-Russian.tsv
- config_name: en_GB-ht
data_files: en_GB-ht.tsv
- config_name: en_GB-ug
data_files: en_GB-ug.tsv
- config_name: en-na
data_files: en-na.tsv
- config_name: en_devel-es_gt
data_files: en_devel-es_gt.tsv
- config_name: en-ka-rGE
data_files: en-ka-rGE.tsv
- config_name: en_US-bn-rBD
data_files: en_US-bn-rBD.tsv
- config_name: eo-ro
data_files: eo-ro.tsv
- config_name: en_GB-ko_KR
data_files: en_GB-ko_KR.tsv
- config_name: en-sr@Latn
data_files: en-sr@Latn.tsv
- config_name: en-french
data_files: en-french.tsv
- config_name: es-nl
data_files: es-nl.tsv
- config_name: en-georgian
data_files: en-georgian.tsv
- config_name: en_devel-sl
data_files: en_devel-sl.tsv
- config_name: en-jv
data_files: en-jv.tsv
- config_name: en-ur-UR
data_files: en-ur-UR.tsv
- config_name: en-dv
data_files: en-dv.tsv
- config_name: en_US-pt-PT
data_files: en_US-pt-PT.tsv
- config_name: en-ar_LY
data_files: en-ar_LY.tsv
- config_name: en-sv-SE
data_files: en-sv-SE.tsv
- config_name: en-ca_ES@valencia
data_files: en-ca_ES@valencia.tsv
- config_name: en_devel-oc
data_files: en_devel-oc.tsv
- config_name: en-th_TH
data_files: en-th_TH.tsv
- config_name: en-de_CH
data_files: en-de_CH.tsv
- config_name: en-ca-valencia
data_files: en-ca-valencia.tsv
- config_name: en-crh
data_files: en-crh.tsv
- config_name: en_US-en@pirate
data_files: en_US-en@pirate.tsv
- config_name: en-haw
data_files: en-haw.tsv
- config_name: en-sk-rSK
data_files: en-sk-rSK.tsv
- config_name: en-sr@latin
data_files: en-sr@latin.tsv
- config_name: en-jam
data_files: en-jam.tsv
- config_name: en_devel-ko
data_files: en_devel-ko.tsv
- config_name: en_devel-de
data_files: en_devel-de.tsv
- config_name: messages-nb_NO
data_files: messages-nb_NO.tsv
- config_name: en_GB-no
data_files: en_GB-no.tsv
- config_name: en_US-tok
data_files: en_US-tok.tsv
- config_name: en_US-zh_Hans
data_files: en_US-zh_Hans.tsv
- config_name: en-hsb
data_files: en-hsb.tsv
- config_name: en-eo
data_files: en-eo.tsv
- config_name: en-eu_ES
data_files: en-eu_ES.tsv
- config_name: en-ayc
data_files: en-ayc.tsv
- config_name: en-ca
data_files: en-ca.tsv
- config_name: en-fr_LU
data_files: en-fr_LU.tsv
- config_name: en-vi-rVN
data_files: en-vi-rVN.tsv
- config_name: en-pr
data_files: en-pr.tsv
- config_name: en-vls
data_files: en-vls.tsv
- config_name: es-gl
data_files: es-gl.tsv
- config_name: en_GB-nb-NO
data_files: en_GB-nb-NO.tsv
- config_name: en_GB-haw
data_files: en_GB-haw.tsv
- config_name: pt_BR-es
data_files: pt_BR-es.tsv
- config_name: en-nn-rNO
data_files: en-nn-rNO.tsv
- config_name: en_US-zh-tw
data_files: en_US-zh-tw.tsv
- config_name: en-ar-AA
data_files: en-ar-AA.tsv
- config_name: en_GB-fr_FR
data_files: en_GB-fr_FR.tsv
- config_name: en_GB-gez
data_files: en_GB-gez.tsv
- config_name: en-ID
data_files: en-ID.tsv
- config_name: en_GB-oc
data_files: en_GB-oc.tsv
- config_name: es-ia
data_files: es-ia.tsv
- config_name: en_GB-kv
data_files: en_GB-kv.tsv
- config_name: en-es-419
data_files: en-es-419.tsv
- config_name: eo-pt
data_files: eo-pt.tsv
- config_name: it-en_EN
data_files: it-en_EN.tsv
- config_name: en-czech
data_files: en-czech.tsv
- config_name: eo-cs
data_files: eo-cs.tsv
- config_name: en_devel-es_sv
data_files: en_devel-es_sv.tsv
- config_name: en-es_CL
data_files: en-es_CL.tsv
- config_name: en-si
data_files: en-si.tsv
- config_name: en-cs
data_files: en-cs.tsv
- config_name: en-sv_SE
data_files: en-sv_SE.tsv
- config_name: en_US-ne_NP
data_files: en_US-ne_NP.tsv
- config_name: en_GB-fy
data_files: en_GB-fy.tsv
- config_name: en_devel-en-rGB
data_files: en_devel-en-rGB.tsv
- config_name: en_GB-sr
data_files: en_GB-sr.tsv
- config_name: en-es-rPE
data_files: en-es-rPE.tsv
- config_name: en_US-en
data_files: en_US-en.tsv
- config_name: en_GB-eu
data_files: en_GB-eu.tsv
- config_name: en_GB-nb_NO
data_files: en_GB-nb_NO.tsv
- config_name: en-uz-UZ
data_files: en-uz-UZ.tsv
- config_name: eo-ko
data_files: eo-ko.tsv
- config_name: en-lb
data_files: en-lb.tsv
- config_name: en-lg
data_files: en-lg.tsv
- config_name: en-Esperanto
data_files: en-Esperanto.tsv
- config_name: en-ar-SA
data_files: en-ar-SA.tsv
- config_name: en_GB-ro_RO
data_files: en_GB-ro_RO.tsv
- config_name: en-cmn
data_files: en-cmn.tsv
- config_name: en-mni@bengali
data_files: en-mni@bengali.tsv
- config_name: en-ks
data_files: en-ks.tsv
- config_name: en_US-pt_BR
data_files: en_US-pt_BR.tsv
- config_name: ru-nb_NO
data_files: ru-nb_NO.tsv
- config_name: en-fr-rCA
data_files: en-fr-rCA.tsv
- config_name: en-kn-rIN
data_files: en-kn-rIN.tsv
- config_name: en_devel-sq_al
data_files: en_devel-sq_al.tsv
- config_name: en_US-nb_NO
data_files: en_US-nb_NO.tsv
- config_name: en-ce
data_files: en-ce.tsv
- config_name: en_US-ga
data_files: en_US-ga.tsv
- config_name: en-en-rZA
data_files: en-en-rZA.tsv
- config_name: en-rue
data_files: en-rue.tsv
- config_name: en-es_CO
data_files: en-es_CO.tsv
- config_name: en-es-es
data_files: en-es-es.tsv
- config_name: en-fa
data_files: en-fa.tsv
- config_name: en-de_DE
data_files: en-de_DE.tsv
- config_name: en-kg
data_files: en-kg.tsv
- config_name: en_US-es_ES
data_files: en_US-es_ES.tsv
- config_name: en-bg-rBG
data_files: en-bg-rBG.tsv
- config_name: fr-nl
data_files: fr-nl.tsv
- config_name: en_GB-as
data_files: en_GB-as.tsv
- config_name: en-nl
data_files: en-nl.tsv
- config_name: en-ka-GE
data_files: en-ka-GE.tsv
- config_name: en-sah
data_files: en-sah.tsv
- config_name: en_US-ur
data_files: en_US-ur.tsv
- config_name: und-si
data_files: und-si.tsv
- config_name: en_devel-en_ca
data_files: en_devel-en_ca.tsv
- config_name: en-cs-CZ
data_files: en-cs-CZ.tsv
- config_name: en-de_DIVEO
data_files: en-de_DIVEO.tsv
- config_name: en-es-PE
data_files: en-es-PE.tsv
- config_name: en-nb-rNO
data_files: en-nb-rNO.tsv
- config_name: en_GB-in
data_files: en_GB-in.tsv
- config_name: en_US-grc
data_files: en_US-grc.tsv
- config_name: en_GB-ast_ES
data_files: en_GB-ast_ES.tsv
- config_name: nb_NO-en
data_files: nb_NO-en.tsv
- config_name: en_devel-zh-cn
data_files: en_devel-zh-cn.tsv
- config_name: en_US-th
data_files: en_US-th.tsv
- config_name: en_devel-fa
data_files: en_devel-fa.tsv
- config_name: en_devel-es_py
data_files: en_devel-es_py.tsv
- config_name: en-prg
data_files: en-prg.tsv
- config_name: en_GB-uk_UA
data_files: en_GB-uk_UA.tsv
- config_name: en-gn
data_files: en-gn.tsv
- config_name: en-sat
data_files: en-sat.tsv
- config_name: en-jpn_JP
data_files: en-jpn_JP.tsv
- config_name: en-ko-rKR
data_files: en-ko-rKR.tsv
- config_name: en-anp
data_files: en-anp.tsv
- config_name: en-si_LK
data_files: en-si_LK.tsv
- config_name: en_GB-gn
data_files: en_GB-gn.tsv
- config_name: en-kn_IN
data_files: en-kn_IN.tsv
- config_name: en-b+jbo
data_files: en-b+jbo.tsv
- config_name: en-me
data_files: en-me.tsv
- config_name: en-lfn
data_files: en-lfn.tsv
- config_name: en-cz
data_files: en-cz.tsv
- config_name: en_GB-iu
data_files: en_GB-iu.tsv
- config_name: en-uz@cyrillic
data_files: en-uz@cyrillic.tsv
- config_name: en_US-es-419
data_files: en_US-es-419.tsv
- config_name: en_US-ug
data_files: en_US-ug.tsv
- config_name: es-ext
data_files: es-ext.tsv
- config_name: en_GB-pa_PK
data_files: en_GB-pa_PK.tsv
- config_name: en-ast
data_files: en-ast.tsv
- config_name: en_US-no
data_files: en_US-no.tsv
- config_name: en-afh
data_files: en-afh.tsv
- config_name: en-fi-rFI
data_files: en-fi-rFI.tsv
- config_name: en-ar-rLY
data_files: en-ar-rLY.tsv
- config_name: en_devel-pt_br
data_files: en_devel-pt_br.tsv
- config_name: en-ca_ES
data_files: en-ca_ES.tsv
- config_name: fr-ru
data_files: fr-ru.tsv
- config_name: en-eo_XX
data_files: en-eo_XX.tsv
- config_name: en_US-tl
data_files: en_US-tl.tsv
- config_name: en_GB-gl
data_files: en_GB-gl.tsv
- config_name: en_UK-es_ES
data_files: en_UK-es_ES.tsv
- config_name: en-be-rBY
data_files: en-be-rBY.tsv
- config_name: en-b+hsb
data_files: en-b+hsb.tsv
- config_name: en_GB-ps
data_files: en_GB-ps.tsv
- config_name: en-hi-IN
data_files: en-hi-IN.tsv
- config_name: en-PL
data_files: en-PL.tsv
- config_name: en_GB-dv
data_files: en_GB-dv.tsv
- config_name: en_US-sv
data_files: en_US-sv.tsv
- config_name: en_US-en_AU
data_files: en_US-en_AU.tsv
- config_name: en_GB-frp
data_files: en_GB-frp.tsv
- config_name: en_GB-sv-SE
data_files: en_GB-sv-SE.tsv
- config_name: en-ZH-rCN
data_files: en-ZH-rCN.tsv
- config_name: en-sq
data_files: en-sq.tsv
- config_name: en-README_FA
data_files: en-README_FA.tsv
- config_name: en_devel-ca
data_files: en_devel-ca.tsv
- config_name: en_UK-fr_FR
data_files: en_UK-fr_FR.tsv
- config_name: en-zh_Hans
data_files: en-zh_Hans.tsv
- config_name: en-ar_DZ
data_files: en-ar_DZ.tsv
- config_name: en-ml
data_files: en-ml.tsv
- config_name: en-zh-rTW
data_files: en-zh-rTW.tsv
- config_name: en-uz-Cyrl
data_files: en-uz-Cyrl.tsv
- config_name: messages-it
data_files: messages-it.tsv
- config_name: en_devel-ru
data_files: en_devel-ru.tsv
- config_name: en-es-MX
data_files: en-es-MX.tsv
- config_name: en_US-zh-Hant-HK
data_files: en_US-zh-Hant-HK.tsv
- config_name: en-de@formal
data_files: en-de@formal.tsv
- config_name: en_US-ar-AA
data_files: en_US-ar-AA.tsv
- config_name: en-en_IE
data_files: en-en_IE.tsv
- config_name: en_US-de
data_files: en_US-de.tsv
- config_name: en-eu
data_files: en-eu.tsv
- config_name: en-tl
data_files: en-tl.tsv
- config_name: ia-ru
data_files: ia-ru.tsv
- config_name: en_GB-my
data_files: en_GB-my.tsv
- config_name: en-Polish
data_files: en-Polish.tsv
- config_name: en_GB-si
data_files: en_GB-si.tsv
- config_name: eo-nb_NO
data_files: eo-nb_NO.tsv
- config_name: en_devel-iw
data_files: en_devel-iw.tsv
- config_name: en_GB-pt_PT
data_files: en_GB-pt_PT.tsv
- config_name: en_GB-tt@iqtelif
data_files: en_GB-tt@iqtelif.tsv
- config_name: en-sk
data_files: en-sk.tsv
- config_name: es-de
data_files: es-de.tsv
- config_name: en-enm
data_files: en-enm.tsv
- config_name: en_US-sk-SK
data_files: en_US-sk-SK.tsv
- config_name: en_GB-be
data_files: en_GB-be.tsv
- config_name: nl-en
data_files: nl-en.tsv
- config_name: en_US-sr_RS
data_files: en_US-sr_RS.tsv
- config_name: en_GB-cy
data_files: en_GB-cy.tsv
- config_name: en_devel-es_uy
data_files: en_devel-es_uy.tsv
- config_name: en-fa-AF
data_files: en-fa-AF.tsv
language:
- aa
- ab
- ace
- ach
- af
- afh
- aii
- ain
- ajp
- ak
- am
- an
- ang
- anp
- apc
- ar
- arn
- ars
- as
- ast
- ay
- ayc
- az
- azb
- ba
- bar
- bd
- be
- bem
- ber
- bg
- bho
- bm
- bn
- bo
- bp
- bqi
- br
- brx
- bs
- bul
- by
- ca
- ce
- ceb
- ckb
- cmn
- cn
- cnr
- co
- cr
- crh
- cs
- csb
- cv
- cy
- cz
- da
- de
- dev
- doi
- dsb
- dua
- dum
- dv
- dz
- eg
- el
- en
- eng
- enm
- eo
- es
- et
- eu
- ext
- fa
- fi
- fil
- fo
- fr
- fra
- frm
- frp
- frs
- fu
- fur
- fy
- ga
- gb
- gd
- gl
- glk
- gmh
- gn
- gr
- gsw
- gu
- guc
- gug
- gum
- guw
- gv
- ha
- haw
- he
- hi
- hne
- hr
- hrx
- hsb
- ht
- hu
- hy
- hz
- ia
- id
- ie
- ig
- in
- io
- is
- it
- iw
- ja
- jam
- jbo
- ji
- jp
- jpn
- jv
- ka
- kab
- kg
- kk
- kl
- km
- kmr
- kn
- ko
- kok
- kr
- krl
- ks
- ksh
- ku
- kw
- ky
- la
- lb
- lfn
- lg
- li
- lk
- ln
- lo
- lt
- ltg
- lv
- lzh
- mai
- me
- mg
- mhr
- mi
- mjw
- mk
- ml
- mn
- mnc
- mni
- mnw
- mo
- mr
- ms
- mt
- my
- na
- nah
- nan
- nap
- nb
- nds
- ne
- nl
- nn
- 'no'
- np
- nqo
- ny
- oc
- oj
- om
- or
- os
- ota
- pa
- pam
- pap
- pbb
- peo
- pk
- pl
- pms
- pr
- prg
- ps
- pt
- pu
- qt
- rcf
- rm
- ro
- rom
- ru
- rue
- rw
- ryu
- sa
- sah
- sai
- sat
- sc
- sco
- sd
- sdh
- se
- sh
- shn
- si
- sk
- skr
- sl
- sm
- sma
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- szl
- ta
- tam
- te
- tet
- tg
- th
- ti
- tk
- tl
- tlh
- tn
- to
- tok
- tr
- trv
- tt
- tum
- tw
- ty
- tzm
- ua
- udm
- ug
- uk
- und
- ur
- us
- uz
- vec
- vi
- vls
- wa
- wae
- wo
- xh
- yi
- yo
- yue
- zgh
- zh
- zu
task_categories:
- translation
- text2text-generation
pretty_name: Weblate Translations
annotations_creators:
- crowdsourced
size_categories:
- 1M<n<10M
license: other
---
# Dataset Card for Weblate Translations
<!-- Provide a quick summary of the dataset. -->
A dataset containing strings from projects hosted on [Weblate](https://hosted.weblate.org) and their translations into other languages.
Please consider [donating](https://weblate.org/en/donate/) or [contributing](https://weblate.org/en/contribute/) to Weblate if you find this dataset useful.
To avoid rows with values like "None" and "N/A" being interpreted as missing values, pass the keep_default_na parameter like this:
```
from datasets import load_dataset
dataset = load_dataset("ayymen/Weblate-Translations", keep_default_na=False)
```
## 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:** Each sentence pair in the dataset has a corresponding license in the "license" column. This license is the one specified in the component or project containing the sentence.
### 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. -->
- Machine Translation
- Language Identification
### 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. -->
- Sentence pairs with empty/missing elements were dropped.
- Identical pairs were dropped.
- Trailing whitespace was stripped.
- Rows were deduplicated based on all 3 columns including "license", on a config/subset/tsv file basis. Which means that a single config might contain two identical sentence pairs with different licenses. Or a different config/subset might contain the exact same row (most likely a different variant/dialect of the same language(s)).
#### 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. -->
Weblate users.
#### 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] |
Sadiksha/go-emotion-dk-autotranlated-10k | ---
dataset_info:
features:
- name: text_en
dtype: string
- name: text
dtype: string
- name: labels
dtype:
class_label:
names:
'0': admiration
'1': amusement
'2': anger
'3': annoyance
'4': approval
'5': caring
'6': confusion
'7': curiosity
'8': desire
'9': disappointment
'10': disapproval
'11': disgust
'12': embarrassment
'13': excitement
'14': fear
'15': gratitude
'16': grief
'17': joy
'18': love
'19': nervousness
'20': neutral
'21': optimism
'22': pride
'23': realization
'24': relief
'25': remorse
'26': sadness
'27': surprise
- name: __index_level_0__
dtype: int64
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 2912227
num_examples: 9000
- name: test
num_bytes: 164591
num_examples: 500
- name: valid
num_bytes: 161062
num_examples: 500
download_size: 1659279
dataset_size: 3237880
---
# Dataset Card for "go-emotion-dk-autotranlated-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Madorashi/SK27MIN | ---
license: unknown
---
|
simustar/stackmathqa200k-instruct | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 263773922
num_examples: 200000
download_size: 140495018
dataset_size: 263773922
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Lojitha/sl_marraige_law_QA | ---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- legal
size_categories:
- 1K<n<10K
---
---
# Lojitha/sl_marriage_law_QA: A Question Answer Dataset on Sri Lankan Marriage Law
---
## Dataset Description
- **Repository:** [Lojitha/sl_marriage_law_QA Repository](https://huggingface.co/datasets/Lojitha/sl_marriage_law_QA)
### Dataset Summary
The `Lojitha/sl_marriage_law_QA` dataset is a collection of question-answer pairs concerning Sri Lankan Marriage Law. It aims to provide a comprehensive resource for understanding the legal aspects of marriage in Sri Lanka. All answers within this dataset have been vetted and verified by legal professionals, ensuring the accuracy and reliability of the information provided.
### Supported Tasks and Leaderboards
- `question-answering`: The dataset can be used to train models for legal question answering tasks, focusing specifically on the domain of marriage law in Sri Lanka.
### Languages
The dataset is presented in English.
## Dataset Structure
### Data Instances
A data instance in the `Lojitha/sl_marriage_law_QA` dataset consists of a pair of a question and its corresponding answer. Below is an example:
```
{
"Question": "Can I register a place of worship for marriages in Sri Lanka?",
"Answer": "Yes, the minister, proprietor, or trustee of any place of worship can apply for it to be registered for the solemnization of marriages."
}
```
### Data Fields
- `Question`: a string containing a question related to Sri Lankan Marriage Law.
- `Answer`: a string containing the answer to the question, verified for accuracy by legal professionals.
### Data Splits
The dataset is provided as a single split without any specific train/validation/test separation. Users are encouraged to create such splits as per their requirements for model training and evaluation.
## Dataset Creation
### Curation Rationale
This dataset was curated to fill the gap in legal question answering resources, specifically targeting the domain of marriage law in Sri Lanka. It is part of a broader research effort aimed at enhancing access to legal information through automated question answering systems.
### Source Data
#### Initial Data Collection and Normalization
Questions were sourced from frequently asked questions in legal forums, consultations, and public inquiries related to marriage law in Sri Lanka. Answers were drafted by legal experts and verified for accuracy and compliance with current law.
#### Who are the source language producers?
The questions and answers were produced by legal professionals with expertise in Sri Lankan marriage law.
### Annotations
#### Annotation process
The answers to the questions were reviewed and verified by multiple legal professionals to ensure accuracy, relevance, and compliance with current Sri Lankan law.
#### Who are the annotators?
The annotators are certified legal professionals specialized in Sri Lankan marriage law.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information. All questions and answers are generic and relate solely to the legal aspects of marriage in Sri Lanka.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset aims to improve access to legal information for the general public, researchers, and professionals interested in Sri Lankan marriage law. It can help in developing automated tools for legal assistance and education.
## Additional Information
### Dataset Curators
The dataset was curated by Lojitha, a researcher focusing on legal informatics. |
fursov/gec_ner_val3 | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 18782227.17508146
num_examples: 55538
- name: validation
num_bytes: 1352747.8249185395
num_examples: 4000
download_size: 4066198
dataset_size: 20134975.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
Alegzandra/REDv2_EN | ---
license: mit
---
|
2Eden2/customsjcode2 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 576372
num_examples: 1288
download_size: 270926
dataset_size: 576372
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Seetha/Visualization | ---
task_categories:
- text-classification
language:
- en
tags:
- finance
pretty_name: visuals
size_categories:
- n<1K
--- |
FINNUMBER/EQA_ORIGINAL | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: doc_id
dtype: string
- name: doc_title
dtype: string
- name: doc_source
dtype: string
- name: doc_published
dtype: int64
- name: created
dtype: string
- name: doc_class
struct:
- name: class
dtype: string
- name: code
dtype: string
- name: paragraphs
list:
- name: context
dtype: string
- name: context_id
dtype: string
- name: qas
list:
- name: answer
struct:
- name: answer_end
dtype: int64
- name: answer_start
dtype: int64
- name: cell_coordinates
dtype: 'null'
- name: cell_text
dtype: 'null'
- name: clue_start
dtype: 'null'
- name: clue_text
dtype: 'null'
- name: options
dtype: 'null'
- name: source
dtype: string
- name: text
dtype: string
- name: qa_type
dtype: int64
- name: question
dtype: string
- name: question_id
dtype: string
- name: tbs
dtype: 'null'
splits:
- name: train
num_bytes: 31028272
num_examples: 14295
- name: test
num_bytes: 7326583
num_examples: 3179
download_size: 16577325
dataset_size: 38354855
---
# Dataset Card for "EQA_ORIGINAL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rexelecaps/Dateset | ---
license: unknown
---
|
azuu/testing | ---
license: apache-2.0
---
|
musiki/dwset | ---
license: other
---
|
open-llm-leaderboard/details_CorticalStack__mistral-7b-tak-stack-dpo | ---
pretty_name: Evaluation run of CorticalStack/mistral-7b-tak-stack-dpo
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CorticalStack/mistral-7b-tak-stack-dpo](https://huggingface.co/CorticalStack/mistral-7b-tak-stack-dpo)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CorticalStack__mistral-7b-tak-stack-dpo\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-01T00:10:38.303986](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-tak-stack-dpo/blob/main/results_2024-03-01T00-10-38.303986.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\": 0.6401786377636143,\n\
\ \"acc_stderr\": 0.032284204036353355,\n \"acc_norm\": 0.6459722449531525,\n\
\ \"acc_norm_stderr\": 0.03293560718942091,\n \"mc1\": 0.2913096695226438,\n\
\ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.4379797481304662,\n\
\ \"mc2_stderr\": 0.014254239933599585\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.575938566552901,\n \"acc_stderr\": 0.014441889627464396,\n\
\ \"acc_norm\": 0.6117747440273038,\n \"acc_norm_stderr\": 0.01424161420741405\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6350328619796853,\n\
\ \"acc_stderr\": 0.004804370563856219,\n \"acc_norm\": 0.8397729535949015,\n\
\ \"acc_norm_stderr\": 0.003660668242740651\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316091,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316091\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\
\ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\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\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224469,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224469\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\
\ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3994708994708995,\n \"acc_stderr\": 0.02522545028406788,\n \"\
acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.02522545028406788\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7548387096774194,\n\
\ \"acc_stderr\": 0.024472243840895525,\n \"acc_norm\": 0.7548387096774194,\n\
\ \"acc_norm_stderr\": 0.024472243840895525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121434,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121434\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.024162780284017724,\n\
\ \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.024162780284017724\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n\
\ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8256880733944955,\n \"acc_stderr\": 0.016265675632010347,\n \"\
acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.016265675632010347\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5787037037037037,\n \"acc_stderr\": 0.033674621388960775,\n \"\
acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.033674621388960775\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\
acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.0364129708131373,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.0364129708131373\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.803680981595092,\n \"acc_stderr\": 0.031207970394709218,\n\
\ \"acc_norm\": 0.803680981595092,\n \"acc_norm_stderr\": 0.031207970394709218\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\
\ \"acc_stderr\": 0.019875655027867464,\n \"acc_norm\": 0.8974358974358975,\n\
\ \"acc_norm_stderr\": 0.019875655027867464\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8135376756066411,\n\
\ \"acc_stderr\": 0.013927751372001505,\n \"acc_norm\": 0.8135376756066411,\n\
\ \"acc_norm_stderr\": 0.013927751372001505\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n\
\ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.329608938547486,\n\
\ \"acc_stderr\": 0.01572153107518388,\n \"acc_norm\": 0.329608938547486,\n\
\ \"acc_norm_stderr\": 0.01572153107518388\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\
\ \"acc_stderr\": 0.026160584450140453,\n \"acc_norm\": 0.6945337620578779,\n\
\ \"acc_norm_stderr\": 0.026160584450140453\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n\
\ \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4471968709256845,\n\
\ \"acc_stderr\": 0.012698825252435108,\n \"acc_norm\": 0.4471968709256845,\n\
\ \"acc_norm_stderr\": 0.012698825252435108\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\
\ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6862745098039216,\n \"acc_stderr\": 0.018771683893528176,\n \
\ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.018771683893528176\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233264\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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2913096695226438,\n\
\ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.4379797481304662,\n\
\ \"mc2_stderr\": 0.014254239933599585\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7932123125493291,\n \"acc_stderr\": 0.011382566829235803\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3858984078847612,\n \
\ \"acc_stderr\": 0.013409077471319177\n }\n}\n```"
repo_url: https://huggingface.co/CorticalStack/mistral-7b-tak-stack-dpo
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|arc:challenge|25_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|gsm8k|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hellaswag|10_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T00-10-38.303986.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T00-10-38.303986.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- '**/details_harness|winogrande|5_2024-03-01T00-10-38.303986.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-01T00-10-38.303986.parquet'
- config_name: results
data_files:
- split: 2024_03_01T00_10_38.303986
path:
- results_2024-03-01T00-10-38.303986.parquet
- split: latest
path:
- results_2024-03-01T00-10-38.303986.parquet
---
# Dataset Card for Evaluation run of CorticalStack/mistral-7b-tak-stack-dpo
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [CorticalStack/mistral-7b-tak-stack-dpo](https://huggingface.co/CorticalStack/mistral-7b-tak-stack-dpo) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CorticalStack__mistral-7b-tak-stack-dpo",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-01T00:10:38.303986](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-tak-stack-dpo/blob/main/results_2024-03-01T00-10-38.303986.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": 0.6401786377636143,
"acc_stderr": 0.032284204036353355,
"acc_norm": 0.6459722449531525,
"acc_norm_stderr": 0.03293560718942091,
"mc1": 0.2913096695226438,
"mc1_stderr": 0.01590598704818483,
"mc2": 0.4379797481304662,
"mc2_stderr": 0.014254239933599585
},
"harness|arc:challenge|25": {
"acc": 0.575938566552901,
"acc_stderr": 0.014441889627464396,
"acc_norm": 0.6117747440273038,
"acc_norm_stderr": 0.01424161420741405
},
"harness|hellaswag|10": {
"acc": 0.6350328619796853,
"acc_stderr": 0.004804370563856219,
"acc_norm": 0.8397729535949015,
"acc_norm_stderr": 0.003660668242740651
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.03860731599316091,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316091
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6867924528301886,
"acc_stderr": 0.028544793319055326,
"acc_norm": 0.6867924528301886,
"acc_norm_stderr": 0.028544793319055326
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"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": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932263,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932263
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.03232146916224469,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224469
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5175438596491229,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.5175438596491229,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5862068965517241,
"acc_stderr": 0.04104269211806232,
"acc_norm": 0.5862068965517241,
"acc_norm_stderr": 0.04104269211806232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3994708994708995,
"acc_stderr": 0.02522545028406788,
"acc_norm": 0.3994708994708995,
"acc_norm_stderr": 0.02522545028406788
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.04403438954768176,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.04403438954768176
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7548387096774194,
"acc_stderr": 0.024472243840895525,
"acc_norm": 0.7548387096774194,
"acc_norm_stderr": 0.024472243840895525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252607,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252607
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.02962022787479048,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.02962022787479048
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8808290155440415,
"acc_stderr": 0.023381935348121434,
"acc_norm": 0.8808290155440415,
"acc_norm_stderr": 0.023381935348121434
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6512820512820513,
"acc_stderr": 0.024162780284017724,
"acc_norm": 0.6512820512820513,
"acc_norm_stderr": 0.024162780284017724
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251976,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251976
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6554621848739496,
"acc_stderr": 0.030868682604121626,
"acc_norm": 0.6554621848739496,
"acc_norm_stderr": 0.030868682604121626
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.03861557546255169,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8256880733944955,
"acc_stderr": 0.016265675632010347,
"acc_norm": 0.8256880733944955,
"acc_norm_stderr": 0.016265675632010347
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5787037037037037,
"acc_stderr": 0.033674621388960775,
"acc_norm": 0.5787037037037037,
"acc_norm_stderr": 0.033674621388960775
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.028626547912437406,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.028626547912437406
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7805907172995781,
"acc_stderr": 0.026939106581553945,
"acc_norm": 0.7805907172995781,
"acc_norm_stderr": 0.026939106581553945
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7786259541984732,
"acc_stderr": 0.0364129708131373,
"acc_norm": 0.7786259541984732,
"acc_norm_stderr": 0.0364129708131373
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.803680981595092,
"acc_stderr": 0.031207970394709218,
"acc_norm": 0.803680981595092,
"acc_norm_stderr": 0.031207970394709218
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.7961165048543689,
"acc_stderr": 0.039891398595317706,
"acc_norm": 0.7961165048543689,
"acc_norm_stderr": 0.039891398595317706
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8974358974358975,
"acc_stderr": 0.019875655027867464,
"acc_norm": 0.8974358974358975,
"acc_norm_stderr": 0.019875655027867464
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8135376756066411,
"acc_stderr": 0.013927751372001505,
"acc_norm": 0.8135376756066411,
"acc_norm_stderr": 0.013927751372001505
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7138728323699421,
"acc_stderr": 0.02433214677913413,
"acc_norm": 0.7138728323699421,
"acc_norm_stderr": 0.02433214677913413
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.329608938547486,
"acc_stderr": 0.01572153107518388,
"acc_norm": 0.329608938547486,
"acc_norm_stderr": 0.01572153107518388
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7581699346405228,
"acc_stderr": 0.024518195641879334,
"acc_norm": 0.7581699346405228,
"acc_norm_stderr": 0.024518195641879334
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6945337620578779,
"acc_stderr": 0.026160584450140453,
"acc_norm": 0.6945337620578779,
"acc_norm_stderr": 0.026160584450140453
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7253086419753086,
"acc_stderr": 0.024836057868294677,
"acc_norm": 0.7253086419753086,
"acc_norm_stderr": 0.024836057868294677
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4471968709256845,
"acc_stderr": 0.012698825252435108,
"acc_norm": 0.4471968709256845,
"acc_norm_stderr": 0.012698825252435108
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6654411764705882,
"acc_stderr": 0.0286619962023353,
"acc_norm": 0.6654411764705882,
"acc_norm_stderr": 0.0286619962023353
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6862745098039216,
"acc_stderr": 0.018771683893528176,
"acc_norm": 0.6862745098039216,
"acc_norm_stderr": 0.018771683893528176
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302506,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302506
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
"acc_stderr": 0.026508590656233264,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.026508590656233264
},
"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": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2913096695226438,
"mc1_stderr": 0.01590598704818483,
"mc2": 0.4379797481304662,
"mc2_stderr": 0.014254239933599585
},
"harness|winogrande|5": {
"acc": 0.7932123125493291,
"acc_stderr": 0.011382566829235803
},
"harness|gsm8k|5": {
"acc": 0.3858984078847612,
"acc_stderr": 0.013409077471319177
}
}
```
## 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] |
tuanmanh28/control_dataset | ---
license: mit
dataset_info:
features:
- name: audio
dtype: audio
- name: file
dtype: string
- name: text
dtype: string
- name: speaker_id
dtype: string
splits:
- name: clean_train
num_bytes: 343258414.1637759
num_examples: 2231
- name: clean_val
num_bytes: 81030474.8752241
num_examples: 558
- name: noise_train
num_bytes: 235319051.21226862
num_examples: 1929
- name: noise_val
num_bytes: 58098436.50373134
num_examples: 483
- name: noise_test
num_bytes: 68465351.0
num_examples: 634
- name: clean_test
num_bytes: 99477037.0
num_examples: 747
download_size: 920722792
dataset_size: 885648764.755
configs:
- config_name: default
data_files:
- split: clean_train
path: data/clean_train-*
- split: clean_val
path: data/clean_val-*
- split: noise_train
path: data/noise_train-*
- split: noise_val
path: data/noise_val-*
- split: clean_test
path: data/clean_test-*
- split: noise_test
path: data/noise_test-*
---
|
Weni/LLM_Base_2.0.0_SFT | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: question
dtype: string
- name: chosen_response
dtype: string
- name: contexto
dtype: string
- name: correct_ans
dtype: int64
splits:
- name: train
num_bytes: 34816176
num_examples: 21621
download_size: 13587497
dataset_size: 34816176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/Mixed_Speech_with_Chinese_and_English_Data_by_Mobile_Phone | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Mixed_Speech_with_Chinese_and_English_Data_by_Mobile_Phone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/939?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data is recorded by 3972 Chinese native speakers with accents covering seven major dialect areas. The recorded text is a mixture of Chinese and English sentences, covering general scenes and human-computer interaction scenes. It is rich in content and accurate in transcription. It can be used for improving the recognition effect of the speech recognition system on Chinese-English mixed reading speech.
For more details, please refer to the link: https://www.nexdata.ai/datasets/939?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Chinese, English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
datajuicer/the-pile-hackernews-refined-by-data-juicer | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 100K<n<1M
---
# The Pile -- HackerNews (refined by Data-Juicer)
A refined version of HackerNews dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-hackernews-refine-result.jsonl) (About 1.8G).
## Dataset Information
- Number of samples: 371,331 (Keep ~99.55% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-HackerNews'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl'
np: 48 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
#- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.2 #<3sigma
- average_line_length_filter:
min_len: 15 # >3sigma
- character_repetition_filter:
rep_len: 10
max_ratio: 0.3 # >3sigma
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.05 # >3sigma
- language_id_score_filter:
min_score: 0.2 # <3sigma
- maximum_line_length_filter:
min_len: 20 # >3sigma
- perplexity_filter:
lang: en
max_ppl: 10000 # >3sigma
- special_characters_filter:
max_ratio: 0.7 # >3sigma
- text_length_filter:
min_len: 100 # > 3sigma
- words_num_filter:
lang: en
tokenization: true
min_num: 30 # > 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.8 # > 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` |
nianqvqv/zidyd | ---
license: mit
---
|
tyzhu/find_last_sent_train_400_eval_40_recite | ---
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: 1367220
num_examples: 840
- name: validation
num_bytes: 72022
num_examples: 40
download_size: 536123
dataset_size: 1439242
---
# Dataset Card for "find_last_sent_train_400_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Lurunchik/WikiHowNFQA | ---
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
tags:
- multi-document NFQA
- non-factoid QA
pretty_name: wikihowqa
size_categories:
- 10K<n<100K
---
# Dataset Card for WikiHowQA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Instances](#data-instances)
- [Data Statistics](#data-statistics)
- [Dataset Information](#dataset-information)
- [Dataset Usage](#dataset-usage)
- [Additional Information](#additional-information)
- [Dataset Curators](#curators)
- [Licensing Information](#license)
- [Citation Information](#citation)
- [Considerations for Using the Data](#considerations)
- [Social Impact of Dataset](#social-impact)
- [Discussion of Biases](#biases)
- [Other Known Limitations](#limitations)
- [Data Loading](#data-loading)
<a name="dataset-description"></a>
## Dataset Description
- **Homepage:** [WikiHowQA Dataset](https://lurunchik.github.io/WikiHowQA/)
- **Repository:** [WikiHowQA Repository](https://github.com/lurunchik/WikiHowQA)
- **Paper:** [WikiHowQA Paper](https://lurunchik.github.io/WikiHowQA/data/ACL_MD_NFQA_dataset.pdf)
- **Leaderboard:** [WikiHowQA Leaderboard](https://lurunchik.github.io/WikiHowQA/leaderboard)
- **Point of Contact:** [Contact](mailto:s3802180@student.rmit.edu.au)
**WikiHowQA** is a unique collection of 'how-to' content from WikiHow, transformed into a rich dataset featuring 11,746 human-authored answers and 74,527 supporting documents. Designed for researchers, it presents a unique opportunity to tackle the challenges of creating comprehensive answers from multiple documents, and grounding those answers in the real-world context provided by the supporting documents.
<a name="dataset-structure"></a>
## Dataset Structure
### Data Fields
- `article_id`: An integer identifier for the article corresponding to article_id from WikHow API.
- `question`: The non-factoid instructional question.
- `answer`: The human-written answer to the question corresponding human-written answer article summary from [WikiHow website](https://www.wikihow.com/Main-Page).
- `related_document_urls_wayback_snapshots`: A list of URLs to web archive snapshots of related documents corresponding references from WikiHow article.
- `split`: The split of the dataset that the instance belongs to ('train', 'validation', or 'test').
- `cluster`: An integer identifier for the cluster that the instance belongs to. <!-- The dataset is split into 'train', 'validation', and 'test' such that all instances from the same cluster belong to the same split. This is to ensure that there is no intersection of paraphrased questions across different splits. If you plan to create a new split of the dataset, it is important to maintain this clustering to avoid data leakage between splits. -->
<a name="dataset-instances"></a>
### Data Instances
An example instance from the WikiHowQA dataset:
```json
{
'article_id': 1353800,
'question': 'How To Cook Pork Tenderloin',
'answer': 'To cook pork tenderloin, put it in a roasting pan and cook it in the oven for 55 minutes at 400 degrees Fahrenheit, turning it over halfway through. You can also sear the pork tenderloin on both sides in a skillet before putting it in the oven, which will reduce the cooking time to 15 minutes. If you want to grill pork tenderloin, start by preheating the grill to medium-high heat. Then, cook the tenderloin on the grill for 30-40 minutes over indirect heat, flipping it occasionally.',
'related_document_urls_wayback_snapshots': ['http://web.archive.org/web/20210605161310/https://www.allrecipes.com/recipe/236114/pork-roast-with-the-worlds-best-rub/', 'http://web.archive.org/web/20210423074902/https://www.bhg.com/recipes/how-to/food-storage-safety/using-a-meat-thermometer/', ...],
'split': 'train',
'cluster': 2635
}
```
<a name="dataset-statistics"></a>
### Dataset Statistics
- Number of human-authored answers: 11,746
- Number of supporting documents: 74,527
- Average number of documents per question: 6.3
- Average number of sentences per answer: 3.9
<a name="dataset-information"></a>
### Dataset Information
The WikiHowQA dataset is divided into two parts: the QA part and the Document Content part.
The QA part of the dataset contains questions, answers, and only links to web archive snapshots of related HTML pages and can be downloaded here.
The Document Content part contains parsed HTML content and is accessible by request and signing a Data Transfer Agreement with RMIT University.
Each dataset instance includes a question, a set of related documents, and a human-authored answer. The questions are non-factoid, requiring comprehensive, multi-sentence answers. The related documents provide the necessary information to generate an answer.
<a name="dataset-usage"></a>
## Dataset Usage
The dataset is designed for researchers and presents a unique opportunity to tackle the challenges of creating comprehensive answers from multiple documents, and grounding those answers in the real-world context provided by the supporting documents.
<a name="additional-information"></a>
## Additional Information
<a name="curators"></a>
### Dataset Curators
The WikiHowQA dataset was curated by researchers at RMIT University.
<a name="license"></a>
### Licensing Information
The QA dataset part is distributed under the Creative Commons Attribution (CC BY) license.
The Dataset Content part containing parsed HTML content is accessible by request and signing a Data Transfer Agreement with RMIT University, which allows using the dataset freely for research purposes. The form to download and sign is available on the dataset website by the link [].
<a name="citation"></a>
### Citation Information
Please cite the following paper if you use this dataset:
```bibtex
@inproceedings{bolotova2023wikihowqa,
title={WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering},
author={Bolotova, Valeriia and Blinov, Vladislav and Filippova, Sofya and Scholer, Falk and Sanderson, Mark},
booktitle="Proceedings of the 61th Conference of the Association for Computational Linguistics",
year={2023}
}
```
<a name="considerations"></a>
## Considerations for Using the Data
<a name="social-impact"></a>
### Social Impact of the Dataset
The WikiHowQA dataset is a rich resource for researchers interested in question answering, information retrieval, and natural language understanding tasks. It can help in developing models that provide comprehensive answers to how-to questions, which can be beneficial in various applications such as customer support, tutoring systems, and personal assistants. However, as with any dataset, the potential for misuse or unintended consequences exists. For example, a model trained on this dataset might be used to generate misleading or incorrect answers if not properly validated.
<a name="biases"></a>
### Discussion of Biases
The WikiHowQA dataset is derived from WikiHow, a community-driven platform. While WikiHow has guidelines to ensure the quality and neutrality of its content, biases could still be present due to the demographic and ideological characteristics of its contributors. Users of the dataset should be aware of this potential bias.
<a name="limitations"></a>
### Other Known Limitations
The dataset only contains 'how-to' questions and their answers. Therefore, it may not be suitable for tasks that require understanding of other types of questions (e.g., why, what, when, who, etc.). Additionally, while the dataset contains a large number of instances, there may still be topics or types of questions that are underrepresented.
<a name="data-loading"></a>
## Data Loading
There are two primary ways to load the QA dataset part:
1. Directly from the file (if you have the .jsonl file locally, you can load the dataset using the following Python code):
```python
import json
dataset = []
with open('wikiHowNFQA.jsonl') as f:
for l in f:
dataset.append(json.loads(l))
```
This will result in a list of dictionaries, each representing a single instance in the dataset.
2. From the Hugging Face Datasets Hub:
If the dataset is hosted on the Hugging Face Datasets Hub, you can load it directly using the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset('wikiHowNFQA')
```
This will return a DatasetDict object, which is a dictionary-like object that maps split names (e.g., 'train', 'validation', 'test') to Dataset objects. You can access a specific split like so: dataset['train']. |
irds/medline_2017 | ---
pretty_name: '`medline/2017`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `medline/2017`
The `medline/2017` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/medline#medline/2017).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=26,740,025
This dataset is used by: [`medline_2017_trec-pm-2017`](https://huggingface.co/datasets/irds/medline_2017_trec-pm-2017), [`medline_2017_trec-pm-2018`](https://huggingface.co/datasets/irds/medline_2017_trec-pm-2018)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/medline_2017', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'abstract': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
|
mHossain/final_train_v4_test_520000 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: input_text
dtype: string
- name: target_text
dtype: string
- name: prefix
dtype: string
splits:
- name: train
num_bytes: 6673807.8
num_examples: 18000
- name: test
num_bytes: 741534.2
num_examples: 2000
download_size: 3192450
dataset_size: 7415342.0
---
# Dataset Card for "final_train_v4_test_520000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/high_elf_archer_goblinslayer | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of High Elf Archer
This is the dataset of High Elf Archer, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 638 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 638 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 638 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 638 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
plncmm/wl-finding | ---
license: cc-by-nc-4.0
---
|
Ehtisham1328/urdu-idioms-with-english-translation | ---
license: apache-2.0
language:
- ur
- en
tags:
- urdu
- idioms
- nlp
- english
size_categories:
- 1K<n<10K
task_categories:
- translation
- text-generation
- text2text-generation
pretty_name: urdu-idioms-with-english-translation
--- |
liuyanchen1015/MULTI_VALUE_sst2_medial_object_perfect | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 1366
num_examples: 10
- name: test
num_bytes: 2809
num_examples: 19
- name: train
num_bytes: 40145
num_examples: 281
download_size: 24014
dataset_size: 44320
---
# Dataset Card for "MULTI_VALUE_sst2_medial_object_perfect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_sst2_drop_aux_be_progressive | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 2563
num_examples: 17
- name: test
num_bytes: 4197
num_examples: 27
- name: train
num_bytes: 56079
num_examples: 491
download_size: 30175
dataset_size: 62839
---
# Dataset Card for "MULTI_VALUE_sst2_drop_aux_be_progressive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/random_letter_same_length_find_passage_train50_eval40_num | ---
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
splits:
- name: train
num_bytes: 43002
num_examples: 140
- name: validation
num_bytes: 15422
num_examples: 40
download_size: 38444
dataset_size: 58424
---
# Dataset Card for "random_letter_same_length_find_passage_train50_eval40_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DFKI-SLT/brat | ---
annotations_creators:
- expert-generated
language_creators:
- found
license: []
task_categories:
- token-classification
task_ids:
- parsing
---
# Information Card for Brat
## Table of Contents
- [Description](#description)
- [Summary](#summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Usage](#usage)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Description
- **Homepage:** https://brat.nlplab.org
- **Paper:** https://aclanthology.org/E12-2021/
- **Leaderboard:** \[Needs More Information\]
- **Point of Contact:** \[Needs More Information\]
### Summary
Brat is an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annota- tion for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. brat is designed in particular for structured annotation, where the notes are not free form text but have a fixed form that can be automatically processed and interpreted by a computer.
## Dataset Structure
Dataset annotated with brat format is processed using this script. Annotations created in brat are stored on disk in a standoff format: annotations are stored separately from the annotated document text, which is never modified by the tool. For each text document in the system, there is a corresponding annotation file. The two are associated by the file naming convention that their base name (file name without suffix) is the same: for example, the file DOC-1000.ann contains annotations for the file DOC-1000.txt. More information can be found [here](https://brat.nlplab.org/standoff.html).
### Data Instances
```
{
"context": ''<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n<Document xmlns:gate="http://www.gat...'
"file_name": "A01"
"spans": {
'id': ['T1', 'T2', 'T4', 'T5', 'T6', 'T3', 'T7', 'T8', 'T9', 'T10', 'T11', 'T12',...]
'type': ['background_claim', 'background_claim', 'background_claim', 'own_claim',...]
'locations': [{'start': [2417], 'end': [2522]}, {'start': [2524], 'end': [2640]},...]
'text': ['complicated 3D character models...', 'The range of breathtaking realistic...', ...]
}
"relations": {
'id': ['R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9', 'R10', 'R11', 'R12',...]
'type': ['supports', 'supports', 'supports', 'supports', 'contradicts', 'contradicts',...]
'arguments': [{'type': ['Arg1', 'Arg2'], 'target': ['T4', 'T5']},...]
}
"equivalence_relations": {'type': [], 'targets': []},
"events": {'id': [], 'type': [], 'trigger': [], 'arguments': []},
"attributions": {'id': [], 'type': [], 'target': [], 'value': []},
"normalizations": {'id': [], 'type': [], 'target': [], 'resource_id': [], 'entity_id': []},
"notes": {'id': [], 'type': [], 'target': [], 'note': []},
}
```
### Data Fields
- `context` (`str`): the textual content of the data file
- `file_name` (`str`): the name of the data / annotation file without extension
- `spans` (`dict`): span annotations of the `context` string
- `id` (`str`): the id of the span, starts with `T`
- `type` (`str`): the label of the span
- `locations` (`list`): the indices indicating the span's locations (multiple because of fragments), consisting of `dict`s with
- `start` (`list` of `int`): the indices indicating the inclusive character start positions of the span fragments
- `end` (`list` of `int`): the indices indicating the exclusive character end positions of the span fragments
- `text` (`list` of `str`): the texts of the span fragments
- `relations`: a sequence of relations between elements of `spans`
- `id` (`str`): the id of the relation, starts with `R`
- `type` (`str`): the label of the relation
- `arguments` (`list` of `dict`): the spans related to the relation, consisting of `dict`s with
- `type` (`list` of `str`): the argument roles of the spans in the relation, either `Arg1` or `Arg2`
- `target` (`list` of `str`): the spans which are the arguments of the relation
- `equivalence_relations`: contains `type` and `target` (more information needed)
- `events`: contains `id`, `type`, `trigger`, and `arguments` (more information needed)
- `attributions` (`dict`): attribute annotations of any other annotation
- `id` (`str`): the instance id of the attribution
- `type` (`str`): the type of the attribution
- `target` (`str`): the id of the annotation to which the attribution is for
- `value` (`str`): the attribution's value or mark
- `normalizations` (`dict`): the unique identification of the real-world entities referred to by specific text expressions
- `id` (`str`): the instance id of the normalized entity
- `type`(`str`): the type of the normalized entity
- `target` (`str`): the id of the annotation to which the normalized entity is for
- `resource_id` (`str`): the associated resource to the normalized entity
- `entity_id` (`str`): the instance id of normalized entity
- `notes` (`dict`): a freeform text, added to the annotation
- `id` (`str`): the instance id of the note
- `type` (`str`): the type of note
- `target` (`str`): the id of the related annotation
- `note` (`str`): the text body of the note
### Usage
The `brat` dataset script can be used by calling `load_dataset()` method and passing any arguments that are accepted by the `BratConfig` (which is a special [BuilderConfig](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.BuilderConfig)). It requires at least the `url` argument. The full list of arguments is as follows:
- `url` (`str`): the url of the dataset which should point to either a zip file or a directory containing the Brat data (`*.txt`) and annotation (`*.ann`) files
- `description` (`str`, optional): the description of the dataset
- `citation` (`str`, optional): the citation of the dataset
- `homepage` (`str`, optional): the homepage of the dataset
- `split_paths` (`dict`, optional): a mapping of (arbitrary) split names to subdirectories or lists of files (without extension), e.g. `{"train": "path/to/train_directory", "test": "path/to/test_director"}` or `{"train": ["path/to/train_file1", "path/to/train_file2"]}`. In both cases (subdirectory paths or file paths), the paths are relative to the url. If `split_paths` is not provided, the dataset will be loaded from the root directory and all direct subfolders will be considered as splits.
- `file_name_blacklist` (`list`, optional): a list of file names (without extension) that should be ignored, e.g. `["A28"]`. This is useful if the dataset contains files that are not valid brat files.
Important: Using the `data_dir` parameter of the `load_dataset()` method overrides the `url` parameter of the `BratConfig`.
We provide an example of [SciArg](https://aclanthology.org/W18-5206.pdf) dataset below:
```python
from datasets import load_dataset
kwargs = {
"description" :
"""This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
scientific writing.""",
"citation" :
"""@inproceedings{lauscher2018b,
title = {An argument-annotated corpus of scientific publications},
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
publisher = {Association for Computational Linguistics},
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
address = {Brussels, Belgium},
year = {2018},
pages = {40–46}
}""",
"homepage": "https://github.com/anlausch/ArguminSci",
"url": "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip",
"split_paths": {
"train": "compiled_corpus",
},
"file_name_blacklist": ['A28'],
}
dataset = load_dataset('dfki-nlp/brat', **kwargs)
```
## Additional Information
### Licensing Information
\[Needs More Information\]
### Citation Information
```
@inproceedings{stenetorp-etal-2012-brat,
title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation",
author = "Stenetorp, Pontus and
Pyysalo, Sampo and
Topi{\'c}, Goran and
Ohta, Tomoko and
Ananiadou, Sophia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the Demonstrations at the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2012",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E12-2021",
pages = "102--107",
}
```
|
kinit/gest | ---
license: apache-2.0
language:
- en
- sl
- sk
- cs
- pl
- sr
- hr
- be
- ru
- uk
tags:
- gender
- gender bias
- gender stereotypes
- stereotypes
- machine translation
- language models
size_categories:
- 1K<n<10K
---
# GEST Dataset
This is a repository for the GEST dataset used to measure gender-stereotypical reasoning in language models and machine translation systems.
- Paper: [Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine Translation and Language Modeling](https://arxiv.org/abs/2311.18711)
- Code and additional data (annotation details, translations) are avialable in [our repository](https://github.com/kinit-sk/gest)
## Stereotypes
The stereotype ids in the dataset represent following stereotypes (the full definition of each stereotype can be found [here](https://github.com/kinit-sk/gest/blob/main/data/data_guidelines.pdf)):
1. Women are emotional and irrational
2. Women are gentle, kind, and submissive
3. Women are empathetic and caring
4. Women are neat and diligent
5. Women are social
6. Women are weak
7. Women are beautiful
8. Men are tough and rough
9. Men are self-confident
10. Men are professional
11. Men are rational
12. Men are providers
13. Men are leaders
14. Men are childish
15. Men are sexual
16. Men are strong |
nateraw/parti-prompts | ---
license: apache-2.0
---
# Dataset Card for PartiPrompts (P2)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://parti.research.google/
- **Repository:** https://github.com/google-research/parti
- **Paper:** https://gweb-research-parti.web.app/parti_paper.pdf
### Dataset Summary
PartiPrompts (P2) is a rich set of over 1600 prompts in English that we release
as part of this work. P2 can be used to measure model capabilities across
various categories and challenge aspects.
P2 prompts can be simple, allowing us to gauge the progress from scaling. They
can also be complex, such as the following 67-word description we created for
Vincent van Gogh’s *The Starry Night* (1889):
*Oil-on-canvas painting of a blue night sky with roiling energy. A fuzzy and
bright yellow crescent moon shining at the top. Below the exploding yellow stars
and radiating swirls of blue, a distant village sits quietly on the right.
Connecting earth and sky is a flame-like cypress tree with curling and swaying
branches on the left. A church spire rises as a beacon over rolling blue hills.*
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text descriptions are in English.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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
The license for this dataset is the apache-2.0 license.
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset. |
CyberHarem/kizuna_elegant_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kizuna_elegant/キズナアイ・エレガント/绊爱·Elegant (Azur Lane)
This is the dataset of kizuna_elegant/キズナアイ・エレガント/绊爱·Elegant (Azur Lane), containing 36 images and their tags.
The core tags of this character are `brown_hair, hairband, multicolored_hair, streaked_hair, long_hair, breasts, pink_hair, pink_hairband, bangs, blue_eyes, medium_breasts, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 36 | 44.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kizuna_elegant_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 36 | 27.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kizuna_elegant_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 79 | 57.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kizuna_elegant_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 36 | 40.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kizuna_elegant_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 79 | 76.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kizuna_elegant_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/kizuna_elegant_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 | 8 |  |  |  |  |  | 1girl, detached_sleeves, looking_at_viewer, solo, virtual_youtuber, bare_shoulders, blush, short_shorts, white_shirt, white_shorts, green_eyes, long_sleeves, navel, sleeveless_shirt, white_background, :d, black_necktie, open_mouth, short_necktie, simple_background, standing, thighhighs, black_sleeves, floating_hair, hand_up, head_tilt, sleeves_past_wrists |
| 1 | 8 |  |  |  |  |  | 1girl, blush, detached_sleeves, solo, virtual_youtuber, looking_at_viewer, navel, thighhighs, black_necktie, open_mouth, white_shorts, bare_shoulders, character_name, teeth, white_background, :d, high_heels, short_shorts, simple_background, white_footwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | detached_sleeves | looking_at_viewer | solo | virtual_youtuber | bare_shoulders | blush | short_shorts | white_shirt | white_shorts | green_eyes | long_sleeves | navel | sleeveless_shirt | white_background | :d | black_necktie | open_mouth | short_necktie | simple_background | standing | thighhighs | black_sleeves | floating_hair | hand_up | head_tilt | sleeves_past_wrists | character_name | teeth | high_heels | white_footwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:--------------------|:-------|:-------------------|:-----------------|:--------|:---------------|:--------------|:---------------|:-------------|:---------------|:--------|:-------------------|:-------------------|:-----|:----------------|:-------------|:----------------|:--------------------|:-----------|:-------------|:----------------|:----------------|:----------|:------------|:----------------------|:-----------------|:--------|:-------------|:-----------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | | X | | | X | | X | X | X | X | | X | | X | | | | | | X | X | X | X |
|
mewsakul/test-project-brand-story-gen-test | ---
dataset_info:
features:
- name: review
dtype: string
- name: Keyword
dtype: string
- name: Anger
dtype: float64
- name: Disgust
dtype: float64
- name: Fear
dtype: float64
- name: Joy
dtype: float64
- name: Neutral
dtype: float64
- name: Sadness
dtype: float64
- name: Surprise
dtype: float64
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 38603.015384615384
num_examples: 58
- name: validation
num_bytes: 4658.984615384616
num_examples: 7
download_size: 48750
dataset_size: 43262.0
---
# Dataset Card for "test-project-brand-story-gen-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_liminerity__mm4-3b | ---
pretty_name: Evaluation run of liminerity/mm4-3b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [liminerity/mm4-3b](https://huggingface.co/liminerity/mm4-3b) 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_liminerity__mm4-3b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-29T19:17:58.857985](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__mm4-3b/blob/main/results_2024-02-29T19-17-58.857985.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\": 0.5089370141170166,\n\
\ \"acc_stderr\": 0.034495031887601606,\n \"acc_norm\": 0.5112482639267588,\n\
\ \"acc_norm_stderr\": 0.035202963761089084,\n \"mc1\": 0.2741738066095471,\n\
\ \"mc1_stderr\": 0.015616518497219373,\n \"mc2\": 0.4319914152632235,\n\
\ \"mc2_stderr\": 0.014565062766855538\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4087030716723549,\n \"acc_stderr\": 0.014365750345427005,\n\
\ \"acc_norm\": 0.44795221843003413,\n \"acc_norm_stderr\": 0.01453201149821167\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5244971121290579,\n\
\ \"acc_stderr\": 0.004983788992681208,\n \"acc_norm\": 0.704142601075483,\n\
\ \"acc_norm_stderr\": 0.0045549440206205\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\": 0.4074074074074074,\n\
\ \"acc_stderr\": 0.042446332383532286,\n \"acc_norm\": 0.4074074074074074,\n\
\ \"acc_norm_stderr\": 0.042446332383532286\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5394736842105263,\n \"acc_stderr\": 0.04056242252249034,\n\
\ \"acc_norm\": 0.5394736842105263,\n \"acc_norm_stderr\": 0.04056242252249034\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5547169811320755,\n \"acc_stderr\": 0.030588052974270655,\n\
\ \"acc_norm\": 0.5547169811320755,\n \"acc_norm_stderr\": 0.030588052974270655\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5694444444444444,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.5694444444444444,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5260115606936416,\n\
\ \"acc_stderr\": 0.03807301726504513,\n \"acc_norm\": 0.5260115606936416,\n\
\ \"acc_norm_stderr\": 0.03807301726504513\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n\
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.03202563076101735,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.03202563076101735\n },\n\
\ \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3508771929824561,\n\
\ \"acc_stderr\": 0.04489539350270699,\n \"acc_norm\": 0.3508771929824561,\n\
\ \"acc_norm_stderr\": 0.04489539350270699\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.35185185185185186,\n \"acc_stderr\": 0.024594975128920945,\n \"\
acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.024594975128920945\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.31746031746031744,\n\
\ \"acc_stderr\": 0.04163453031302859,\n \"acc_norm\": 0.31746031746031744,\n\
\ \"acc_norm_stderr\": 0.04163453031302859\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6225806451612903,\n\
\ \"acc_stderr\": 0.027575960723278243,\n \"acc_norm\": 0.6225806451612903,\n\
\ \"acc_norm_stderr\": 0.027575960723278243\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.39408866995073893,\n \"acc_stderr\": 0.03438157967036545,\n\
\ \"acc_norm\": 0.39408866995073893,\n \"acc_norm_stderr\": 0.03438157967036545\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.5393939393939394,\n \"acc_stderr\": 0.03892207016552012,\n\
\ \"acc_norm\": 0.5393939393939394,\n \"acc_norm_stderr\": 0.03892207016552012\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6464646464646465,\n \"acc_stderr\": 0.03406086723547155,\n \"\
acc_norm\": 0.6464646464646465,\n \"acc_norm_stderr\": 0.03406086723547155\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.694300518134715,\n \"acc_stderr\": 0.033248379397581594,\n\
\ \"acc_norm\": 0.694300518134715,\n \"acc_norm_stderr\": 0.033248379397581594\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4794871794871795,\n \"acc_stderr\": 0.025329663163489943,\n\
\ \"acc_norm\": 0.4794871794871795,\n \"acc_norm_stderr\": 0.025329663163489943\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2518518518518518,\n \"acc_stderr\": 0.026466117538959916,\n \
\ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.026466117538959916\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.542016806722689,\n \"acc_stderr\": 0.03236361111951941,\n \
\ \"acc_norm\": 0.542016806722689,\n \"acc_norm_stderr\": 0.03236361111951941\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\
acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6495412844036698,\n \"acc_stderr\": 0.02045607759982446,\n \"\
acc_norm\": 0.6495412844036698,\n \"acc_norm_stderr\": 0.02045607759982446\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3333333333333333,\n \"acc_stderr\": 0.03214952147802749,\n \"\
acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.03214952147802749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239171,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239171\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6455696202531646,\n \"acc_stderr\": 0.031137304297185812,\n \
\ \"acc_norm\": 0.6455696202531646,\n \"acc_norm_stderr\": 0.031137304297185812\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5739910313901345,\n\
\ \"acc_stderr\": 0.033188332862172806,\n \"acc_norm\": 0.5739910313901345,\n\
\ \"acc_norm_stderr\": 0.033188332862172806\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.043285772152629715,\n\
\ \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.043285772152629715\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6528925619834711,\n \"acc_stderr\": 0.043457245702925335,\n \"\
acc_norm\": 0.6528925619834711,\n \"acc_norm_stderr\": 0.043457245702925335\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5648148148148148,\n\
\ \"acc_stderr\": 0.04792898170907061,\n \"acc_norm\": 0.5648148148148148,\n\
\ \"acc_norm_stderr\": 0.04792898170907061\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5950920245398773,\n \"acc_stderr\": 0.038566721635489125,\n\
\ \"acc_norm\": 0.5950920245398773,\n \"acc_norm_stderr\": 0.038566721635489125\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\
\ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\
\ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.04689765937278135,\n\
\ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.04689765937278135\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8076923076923077,\n\
\ \"acc_stderr\": 0.02581923325648371,\n \"acc_norm\": 0.8076923076923077,\n\
\ \"acc_norm_stderr\": 0.02581923325648371\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562429,\n \
\ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562429\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6436781609195402,\n\
\ \"acc_stderr\": 0.017125853762755893,\n \"acc_norm\": 0.6436781609195402,\n\
\ \"acc_norm_stderr\": 0.017125853762755893\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5722543352601156,\n \"acc_stderr\": 0.026636539741116072,\n\
\ \"acc_norm\": 0.5722543352601156,\n \"acc_norm_stderr\": 0.026636539741116072\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.26256983240223464,\n\
\ \"acc_stderr\": 0.014716824273017756,\n \"acc_norm\": 0.26256983240223464,\n\
\ \"acc_norm_stderr\": 0.014716824273017756\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5718954248366013,\n \"acc_stderr\": 0.028332397483664278,\n\
\ \"acc_norm\": 0.5718954248366013,\n \"acc_norm_stderr\": 0.028332397483664278\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.594855305466238,\n\
\ \"acc_stderr\": 0.02788238379132595,\n \"acc_norm\": 0.594855305466238,\n\
\ \"acc_norm_stderr\": 0.02788238379132595\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5370370370370371,\n \"acc_stderr\": 0.027744313443376536,\n\
\ \"acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.027744313443376536\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3546099290780142,\n \"acc_stderr\": 0.02853865002887864,\n \
\ \"acc_norm\": 0.3546099290780142,\n \"acc_norm_stderr\": 0.02853865002887864\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.38461538461538464,\n\
\ \"acc_stderr\": 0.012425548416302945,\n \"acc_norm\": 0.38461538461538464,\n\
\ \"acc_norm_stderr\": 0.012425548416302945\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.45588235294117646,\n \"acc_stderr\": 0.030254372573976684,\n\
\ \"acc_norm\": 0.45588235294117646,\n \"acc_norm_stderr\": 0.030254372573976684\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4722222222222222,\n \"acc_stderr\": 0.020196594933541208,\n \
\ \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.020196594933541208\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n\
\ \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n\
\ \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5959183673469388,\n \"acc_stderr\": 0.03141470802586589,\n\
\ \"acc_norm\": 0.5959183673469388,\n \"acc_norm_stderr\": 0.03141470802586589\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7512437810945274,\n\
\ \"acc_stderr\": 0.030567675938916714,\n \"acc_norm\": 0.7512437810945274,\n\
\ \"acc_norm_stderr\": 0.030567675938916714\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7251461988304093,\n \"acc_stderr\": 0.03424042924691583,\n\
\ \"acc_norm\": 0.7251461988304093,\n \"acc_norm_stderr\": 0.03424042924691583\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2741738066095471,\n\
\ \"mc1_stderr\": 0.015616518497219373,\n \"mc2\": 0.4319914152632235,\n\
\ \"mc2_stderr\": 0.014565062766855538\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6621941594317285,\n \"acc_stderr\": 0.013292583502910885\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4382107657316149,\n \
\ \"acc_stderr\": 0.013666915917255072\n }\n}\n```"
repo_url: https://huggingface.co/liminerity/mm4-3b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|arc:challenge|25_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|gsm8k|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hellaswag|10_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-17-58.857985.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T19-17-58.857985.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- '**/details_harness|winogrande|5_2024-02-29T19-17-58.857985.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-29T19-17-58.857985.parquet'
- config_name: results
data_files:
- split: 2024_02_29T19_17_58.857985
path:
- results_2024-02-29T19-17-58.857985.parquet
- split: latest
path:
- results_2024-02-29T19-17-58.857985.parquet
---
# Dataset Card for Evaluation run of liminerity/mm4-3b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [liminerity/mm4-3b](https://huggingface.co/liminerity/mm4-3b) 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_liminerity__mm4-3b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-29T19:17:58.857985](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__mm4-3b/blob/main/results_2024-02-29T19-17-58.857985.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": 0.5089370141170166,
"acc_stderr": 0.034495031887601606,
"acc_norm": 0.5112482639267588,
"acc_norm_stderr": 0.035202963761089084,
"mc1": 0.2741738066095471,
"mc1_stderr": 0.015616518497219373,
"mc2": 0.4319914152632235,
"mc2_stderr": 0.014565062766855538
},
"harness|arc:challenge|25": {
"acc": 0.4087030716723549,
"acc_stderr": 0.014365750345427005,
"acc_norm": 0.44795221843003413,
"acc_norm_stderr": 0.01453201149821167
},
"harness|hellaswag|10": {
"acc": 0.5244971121290579,
"acc_stderr": 0.004983788992681208,
"acc_norm": 0.704142601075483,
"acc_norm_stderr": 0.0045549440206205
},
"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": 0.4074074074074074,
"acc_stderr": 0.042446332383532286,
"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.042446332383532286
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5394736842105263,
"acc_stderr": 0.04056242252249034,
"acc_norm": 0.5394736842105263,
"acc_norm_stderr": 0.04056242252249034
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5547169811320755,
"acc_stderr": 0.030588052974270655,
"acc_norm": 0.5547169811320755,
"acc_norm_stderr": 0.030588052974270655
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5694444444444444,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.5694444444444444,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5260115606936416,
"acc_stderr": 0.03807301726504513,
"acc_norm": 0.5260115606936416,
"acc_norm_stderr": 0.03807301726504513
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4,
"acc_stderr": 0.03202563076101735,
"acc_norm": 0.4,
"acc_norm_stderr": 0.03202563076101735
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3508771929824561,
"acc_stderr": 0.04489539350270699,
"acc_norm": 0.3508771929824561,
"acc_norm_stderr": 0.04489539350270699
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4689655172413793,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.4689655172413793,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.024594975128920945,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.024594975128920945
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.31746031746031744,
"acc_stderr": 0.04163453031302859,
"acc_norm": 0.31746031746031744,
"acc_norm_stderr": 0.04163453031302859
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6225806451612903,
"acc_stderr": 0.027575960723278243,
"acc_norm": 0.6225806451612903,
"acc_norm_stderr": 0.027575960723278243
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.39408866995073893,
"acc_stderr": 0.03438157967036545,
"acc_norm": 0.39408866995073893,
"acc_norm_stderr": 0.03438157967036545
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.5393939393939394,
"acc_stderr": 0.03892207016552012,
"acc_norm": 0.5393939393939394,
"acc_norm_stderr": 0.03892207016552012
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6464646464646465,
"acc_stderr": 0.03406086723547155,
"acc_norm": 0.6464646464646465,
"acc_norm_stderr": 0.03406086723547155
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.694300518134715,
"acc_stderr": 0.033248379397581594,
"acc_norm": 0.694300518134715,
"acc_norm_stderr": 0.033248379397581594
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4794871794871795,
"acc_stderr": 0.025329663163489943,
"acc_norm": 0.4794871794871795,
"acc_norm_stderr": 0.025329663163489943
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2518518518518518,
"acc_stderr": 0.026466117538959916,
"acc_norm": 0.2518518518518518,
"acc_norm_stderr": 0.026466117538959916
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.542016806722689,
"acc_stderr": 0.03236361111951941,
"acc_norm": 0.542016806722689,
"acc_norm_stderr": 0.03236361111951941
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2781456953642384,
"acc_stderr": 0.03658603262763743,
"acc_norm": 0.2781456953642384,
"acc_norm_stderr": 0.03658603262763743
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6495412844036698,
"acc_stderr": 0.02045607759982446,
"acc_norm": 0.6495412844036698,
"acc_norm_stderr": 0.02045607759982446
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.03214952147802749,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.03214952147802749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.03460228327239171,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.03460228327239171
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6455696202531646,
"acc_stderr": 0.031137304297185812,
"acc_norm": 0.6455696202531646,
"acc_norm_stderr": 0.031137304297185812
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5739910313901345,
"acc_stderr": 0.033188332862172806,
"acc_norm": 0.5739910313901345,
"acc_norm_stderr": 0.033188332862172806
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5801526717557252,
"acc_stderr": 0.043285772152629715,
"acc_norm": 0.5801526717557252,
"acc_norm_stderr": 0.043285772152629715
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6528925619834711,
"acc_stderr": 0.043457245702925335,
"acc_norm": 0.6528925619834711,
"acc_norm_stderr": 0.043457245702925335
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5648148148148148,
"acc_stderr": 0.04792898170907061,
"acc_norm": 0.5648148148148148,
"acc_norm_stderr": 0.04792898170907061
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5950920245398773,
"acc_stderr": 0.038566721635489125,
"acc_norm": 0.5950920245398773,
"acc_norm_stderr": 0.038566721635489125
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.3125,
"acc_stderr": 0.043994650575715215,
"acc_norm": 0.3125,
"acc_norm_stderr": 0.043994650575715215
},
"harness|hendrycksTest-management|5": {
"acc": 0.6601941747572816,
"acc_stderr": 0.04689765937278135,
"acc_norm": 0.6601941747572816,
"acc_norm_stderr": 0.04689765937278135
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8076923076923077,
"acc_stderr": 0.02581923325648371,
"acc_norm": 0.8076923076923077,
"acc_norm_stderr": 0.02581923325648371
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562429,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562429
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6436781609195402,
"acc_stderr": 0.017125853762755893,
"acc_norm": 0.6436781609195402,
"acc_norm_stderr": 0.017125853762755893
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5722543352601156,
"acc_stderr": 0.026636539741116072,
"acc_norm": 0.5722543352601156,
"acc_norm_stderr": 0.026636539741116072
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.26256983240223464,
"acc_stderr": 0.014716824273017756,
"acc_norm": 0.26256983240223464,
"acc_norm_stderr": 0.014716824273017756
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5718954248366013,
"acc_stderr": 0.028332397483664278,
"acc_norm": 0.5718954248366013,
"acc_norm_stderr": 0.028332397483664278
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.594855305466238,
"acc_stderr": 0.02788238379132595,
"acc_norm": 0.594855305466238,
"acc_norm_stderr": 0.02788238379132595
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5370370370370371,
"acc_stderr": 0.027744313443376536,
"acc_norm": 0.5370370370370371,
"acc_norm_stderr": 0.027744313443376536
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3546099290780142,
"acc_stderr": 0.02853865002887864,
"acc_norm": 0.3546099290780142,
"acc_norm_stderr": 0.02853865002887864
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.38461538461538464,
"acc_stderr": 0.012425548416302945,
"acc_norm": 0.38461538461538464,
"acc_norm_stderr": 0.012425548416302945
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.45588235294117646,
"acc_stderr": 0.030254372573976684,
"acc_norm": 0.45588235294117646,
"acc_norm_stderr": 0.030254372573976684
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.020196594933541208,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.020196594933541208
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5818181818181818,
"acc_stderr": 0.04724577405731572,
"acc_norm": 0.5818181818181818,
"acc_norm_stderr": 0.04724577405731572
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5959183673469388,
"acc_stderr": 0.03141470802586589,
"acc_norm": 0.5959183673469388,
"acc_norm_stderr": 0.03141470802586589
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7512437810945274,
"acc_stderr": 0.030567675938916714,
"acc_norm": 0.7512437810945274,
"acc_norm_stderr": 0.030567675938916714
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-virology|5": {
"acc": 0.45180722891566266,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.45180722891566266,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7251461988304093,
"acc_stderr": 0.03424042924691583,
"acc_norm": 0.7251461988304093,
"acc_norm_stderr": 0.03424042924691583
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2741738066095471,
"mc1_stderr": 0.015616518497219373,
"mc2": 0.4319914152632235,
"mc2_stderr": 0.014565062766855538
},
"harness|winogrande|5": {
"acc": 0.6621941594317285,
"acc_stderr": 0.013292583502910885
},
"harness|gsm8k|5": {
"acc": 0.4382107657316149,
"acc_stderr": 0.013666915917255072
}
}
```
## 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] |
tomc43841/public_smash_little_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: Start
dtype: bool
- name: A
dtype: bool
- name: B
dtype: bool
- name: X
dtype: bool
- name: Y
dtype: bool
- name: Z
dtype: bool
- name: DPadUp
dtype: bool
- name: DPadDown
dtype: bool
- name: DPadLeft
dtype: bool
- name: DPadRight
dtype: bool
- name: L
dtype: bool
- name: R
dtype: bool
- name: LPressure
dtype: int64
- name: RPressure
dtype: int64
- name: XAxis
dtype: int64
- name: YAxis
dtype: int64
- name: CXAxis
dtype: int64
- name: CYAxis
dtype: int64
splits:
- name: train
num_bytes: 847626439.17
num_examples: 9127
download_size: 695989419
dataset_size: 847626439.17
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/neve_nikke | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of neve/ネヴェ/尼夫/네베 (Nikke: Goddess of Victory)
This is the dataset of neve/ネヴェ/尼夫/네베 (Nikke: Goddess of Victory), containing 31 images and their tags.
The core tags of this character are `bangs, breasts, grey_hair, large_breasts, mole, mole_on_breast`, 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 | 56.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neve_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 31 | 27.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neve_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 80 | 58.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neve_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 31 | 47.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neve_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 80 | 93.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neve_nikke/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/neve_nikke',
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 | 5 |  |  |  |  |  | 1girl, black_bodysuit, blush, gloves, looking_at_viewer, open_mouth, sleep_mask, solo, bear_costume, cleavage, long_sleeves, zipper, animal_hood, covered_navel, fur_trim, hood_up, mask_on_head, :d, cowboy_shot, headphones_around_neck, mole_under_mouth, pouch, standing, teeth, tongue |
| 1 | 6 |  |  |  |  |  | 1girl, animal_hood, cleavage, looking_at_viewer, open_mouth, solo, smile, animal_costume, black_bodysuit, blush, huge_breasts, simple_background, white_background, zipper, black_gloves, open_clothes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bodysuit | blush | gloves | looking_at_viewer | open_mouth | sleep_mask | solo | bear_costume | cleavage | long_sleeves | zipper | animal_hood | covered_navel | fur_trim | hood_up | mask_on_head | :d | cowboy_shot | headphones_around_neck | mole_under_mouth | pouch | standing | teeth | tongue | smile | animal_costume | huge_breasts | simple_background | white_background | black_gloves | open_clothes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:---------|:--------------------|:-------------|:-------------|:-------|:---------------|:-----------|:---------------|:---------|:--------------|:----------------|:-----------|:----------|:---------------|:-----|:--------------|:-------------------------|:-------------------|:--------|:-----------|:--------|:---------|:--------|:-----------------|:---------------|:--------------------|:-------------------|:---------------|:---------------|
| 0 | 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 | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | X | X | | X | | X | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X |
|
GEM-submissions/lewtun__this-is-a-test__1647263213 | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
|
Rossil/realnewslike_with_title | ---
language: en
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: timestamp[s]
- name: url
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 38733473854
num_examples: 13813701
download_size: 24654646282
dataset_size: 38733473854
---
# Dataset Card for "realnewslike_with_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
freddyaboulton/new_saving_json_3 | ---
configs:
- config_name: default
data_files:
- split: train
path: '**/*.jsonl'
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
nateraw/hyperbard | ---
zenodo_id: '6627159'
license:
- unknown
---
# Dataset Card for Hyperbard (Dataset)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://zenodo.org/record/6627159
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
<p>First release of <a href="https://hyperbard.net">Hyperbard</a>.</p>
<p>Hyperbard is a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication settings and character contributions as hyperedges with edge-specific node weights. By making multiple intuitive representations readily available for experimentation, we facilitate rigorous representation robustness checks in graph learning, graph mining, and network analysis, highlighting the advantages and drawbacks of specific representations. </p>
<p>The code used to create Hyperbard is maintained on <a href="https://github.com/hyperbard/hyperbard">GitHub</a>. </p>
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The class labels in the dataset are in English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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
This dataset was shared by Corinna Coupette, Jilles Vreeken, Bastian Rieck
### Licensing Information
The license for this dataset is http://creativecommons.org/licenses/by-nc/2.0/
### Citation Information
```bibtex
@dataset{corinna_coupette_2022_6627159,
author = {Corinna Coupette and
Jilles Vreeken and
Bastian Rieck},
title = {Hyperbard (Dataset)},
month = jun,
year = 2022,
publisher = {Zenodo},
version = {0.0.1},
doi = {10.5281/zenodo.6627159},
url = {https://doi.org/10.5281/zenodo.6627159}
}
```
### Contributions
[More Information Needed] |
HossainRabby/evaluation | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: predicted_answer
dtype: string
- name: target_answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int32
splits:
- name: train
num_bytes: 102827
num_examples: 82
download_size: 0
dataset_size: 102827
---
# Dataset Card for "evaluation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lhoestq/tmp2 | ---
configs:
- config_name: default
data_files: data.txt
--- |
lmms-lab/SEED-Bench-2 | ---
dataset_info:
features:
- name: answer
dtype: string
- name: choice_a
dtype: string
- name: choice_b
dtype: string
- name: choice_c
dtype: string
- name: choice_d
dtype: string
- name: data_id
sequence: string
- name: data_type
dtype: string
- name: data_source
dtype: string
- name: level
dtype: string
- name: question
dtype: string
- name: question_id
dtype: string
- name: question_type_id
dtype: int16
- name: image
sequence: image
- name: subpart
dtype: string
- name: version
dtype: string
splits:
- name: test
num_bytes: 41770062282.022
num_examples: 24371
download_size: 38037968494
dataset_size: 41770062282.022
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [SEED-Bench-2](https://github.com/AILab-CVC/SEED-Bench). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{li2023seed2,
title={SEED-Bench-2: Benchmarking Multimodal Large Language Models},
author={Li, Bohao and Ge, Yuying and Ge, Yixiao and Wang, Guangzhi and Wang, Rui and Zhang, Ruimao and Shan, Ying},
journal={arXiv preprint arXiv:2311.17092},
year={2023}
}
``` |
acul3/KoPI-CC | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license: cc
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
---
### Dataset Summary
KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian only extract from Common Crawl snapshots using [ungoliant](https://github.com/oscar-corpus/ungoliant), each snapshot also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup
### Preprocessing
Each folder name inside snapshots folder denoted preprocessing technique that has been applied .
- **Raw**
- this processed directly from cc snapshot using ungoliant without any addition filter ,you can read it in their paper (citation below)
- use same "raw cc snapshot" for `2021_10` and `2021_49` which can be found in oscar dataset ([2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/tree/main/packaged_nondedup/id) and [2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201/tree/main/compressed/id_meta))
- **Dedup**
- use data from raw folder
- apply cleaning techniques for every text in documents such as
- fix html
- remove noisy unicode
- fix news tag
- remove control char
- filter by removing short text (20 words)
- filter by character ratio occurred inside text such as
- min_alphabet_ratio (0.75)
- max_upper_ratio (0.10)
- max_number_ratio(0.05)
- filter by exact dedup technique
- hash all text with md5 hashlib
- remove non-unique hash
- full code about dedup step adapted from [here](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned/tree/main)
- **Neardup**
- use data from dedup folder
- create index cluster using neardup [Minhash and LSH](http://ekzhu.com/datasketch/lsh.html) with following config :
- use 128 permuation
- 6 n-grams size
- use word tokenization (split sentence by space)
- use 0.8 as similarity score
- fillter by removing all index from cluster
- full code about neardup step adapted from [here](https://github.com/ChenghaoMou/text-dedup)
- **Neardup_clean**
- use data from neardup folder
- Removing documents containing words from a selection of the [Indonesian Bad Words](https://github.com/acul3/c4_id_processed/blob/67e10c086d43152788549ef05b7f09060e769993/clean/badwords_ennl.py#L64).
- Removing sentences containing:
- Less than 3 words.
- A word longer than 1000 characters.
- An end symbol not matching end-of-sentence punctuation.
- Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in indonesia
- Removing documents (after sentence filtering):
- Containing less than 5 sentences.
- Containing less than 500 or more than 50'000 characters.
- full code about neardup_clean step adapted from [here](https://gitlab.com/yhavinga/c4nlpreproc)
## Dataset Structure
### Data Instances
An example from the dataset:
```
{'text': 'Panitia Kerja (Panja) pembahasan RUU Cipta Kerja (Ciptaker) DPR RI memastikan naskah UU Ciptaker sudah final, tapi masih dalam penyisiran. Penyisiran dilakukan agar isi UU Ciptaker sesuai dengan kesepakatan dalam pembahasan dan tidak ada salah pengetikan (typo).\n"Kan memang sudah diumumkan, naskah final itu sudah. Cuma kita sekarang … DPR itu kan punya waktu 7 hari sebelum naskah resminya kita kirim ke pemerintah. Nah, sekarang itu kita sisir, jangan sampai ada yang salah pengetikan, tapi tidak mengubah substansi," kata Ketua Panja RUU Ciptaker Supratman Andi Agtas saat berbincang dengan detikcom, Jumat (9/10/2020) pukul 10.56 WIB.\nSupratman mengungkapkan Panja RUU Ciptaker menggelar rapat hari ini untuk melakukan penyisiran terhadap naskah UU Ciptaker. Panja, sebut dia, bekerja sama dengan pemerintah dan ahli bahasa untuk melakukan penyisiran naskah.\n"Sebentar, siang saya undang seluruh poksi-poksi (kelompok fraksi) Baleg (Badan Legislasi DPR), anggota Panja itu datang ke Baleg untuk melihat satu per satu, jangan sampai …. Karena kan sekarang ini tim dapur pemerintah dan DPR lagi bekerja bersama dengan ahli bahasa melihat jangan sampai ada yang typo, redundant," terangnya.\nSupratman membenarkan bahwa naskah UU Ciptaker yang final itu sudah beredar. Ketua Baleg DPR itu memastikan penyisiran yang dilakukan tidak mengubah substansi setiap pasal yang telah melalui proses pembahasan.\n"Itu yang sudah dibagikan. Tapi kan itu substansinya yang tidak mungkin akan berubah. Nah, kita pastikan nih dari sisi drafting-nya yang jadi kita pastikan," tutur Supratman.\nLebih lanjut Supratman menjelaskan DPR memiliki waktu 7 hari untuk melakukan penyisiran. Anggota DPR dari Fraksi Gerindra itu memastikan paling lambat Selasa (13/10) pekan depan, naskah UU Ciptaker sudah bisa diakses oleh masyarakat melalui situs DPR.\n"Kita itu, DPR, punya waktu sampai 7 hari kerja. Jadi harusnya hari Selasa sudah final semua, paling lambat. Tapi saya usahakan hari ini bisa final. Kalau sudah final, semua itu langsung bisa diakses di web DPR," terang Supratman.\nDiberitakan sebelumnya, Wakil Ketua Baleg DPR Achmad Baidowi mengakui naskah UU Ciptaker yang telah disahkan di paripurna DPR masih dalam proses pengecekan untuk menghindari kesalahan pengetikan. Anggota Komisi VI DPR itu menyinggung soal salah ketik dalam revisi UU KPK yang disahkan pada 2019.\n"Mengoreksi yang typo itu boleh, asalkan tidak mengubah substansi. Jangan sampai seperti tahun lalu, ada UU salah ketik soal umur \'50 (empat puluh)\', sehingga pemerintah harus mengonfirmasi lagi ke DPR," ucap Baidowi, Kamis (8/10).',
'url': 'https://news.detik.com/berita/d-5206925/baleg-dpr-naskah-final-uu-ciptaker-sedang-diperbaiki-tanpa-ubah-substansi?tag_from=wp_cb_mostPopular_list&_ga=2.71339034.848625040.1602222726-629985507.1602222726',
'timestamp': '2021-10-22T04:09:47Z',
'meta': '{"warc_headers": {"content-length": "2747", "content-type": "text/plain", "warc-date": "2021-10-22T04:09:47Z", "warc-record-id": "<urn:uuid:a5b2cc09-bd2b-4d0e-9e5b-2fcc5fce47cb>", "warc-identified-content-language": "ind,eng", "warc-target-uri": "https://news.detik.com/berita/d-5206925/baleg-dpr-naskah-final-uu-ciptaker-sedang-diperbaiki-tanpa-ubah-substansi?tag_from=wp_cb_mostPopular_list&_ga=2.71339034.848625040.1602222726-629985507.1602222726", "warc-block-digest": "sha1:65AWBDBLS74AGDCGDBNDHBHADOKSXCKV", "warc-type": "conversion", "warc-refers-to": "<urn:uuid:b7ceadba-7120-4e38-927c-a50db21f0d4f>"}, "identification": {"label": "id", "prob": 0.6240405}, "annotations": null, "line_identifications": [null, {"label": "id", "prob": 0.9043896}, null, null, {"label": "id", "prob": 0.87111086}, {"label": "id", "prob": 0.9095224}, {"label": "id", "prob": 0.8579232}, {"label": "id", "prob": 0.81366056}, {"label": "id", "prob": 0.9286813}, {"label": "id", "prob": 0.8435194}, {"label": "id", "prob": 0.8387821}, null]}'}
```
### Data Fields
The data contains the following fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp of extraction as a string
- `meta` : json representation of the original from ungoliant tools,can be found [here](https://oscar-corpus.com/post/oscar-v22-01/) (warc_heder)
## Additional Information
### Dataset Curators
For inquiries or requests regarding the KoPI-CC contained in this repository, please contact me at [samsulrahmadani@gmail.com](mailto:samsulrahmadani@gmail.com)
### Licensing Information
These data are released under this licensing scheme
I do not own any of the text from which these data has been extracted.
the license actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
Should you consider that data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
I will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = jan,
eid = {arXiv:2201.06642},
pages = {arXiv:2201.06642},
archivePrefix = {arXiv},
eprint = {2201.06642},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220106642A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
``` |
nongiga/nt_multispecies_16384 | ---
dataset_info:
features:
- name: sequence
dtype: string
- name: description
dtype: string
- name: start_pos
dtype: int32
- name: end_pos
dtype: int32
- name: fasta_url
dtype: string
splits:
- name: train
num_bytes: 133253293053
num_examples: 127050
- name: validation
num_bytes: 53488531
num_examples: 51
- name: test
num_bytes: 84951962
num_examples: 81
download_size: 60060196924
dataset_size: 133391733546
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_185 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 742912616.0
num_examples: 145898
download_size: 757567739
dataset_size: 742912616.0
---
# Dataset Card for "chunk_185"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_cola_existential_it | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 1517
num_examples: 22
- name: test
num_bytes: 1576
num_examples: 21
- name: train
num_bytes: 9905
num_examples: 146
download_size: 12084
dataset_size: 12998
---
# Dataset Card for "MULTI_VALUE_cola_existential_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
L1vs1/Tyler_Durden | ---
license: unknown
---
|
EduardoPacheco/seggpt-example-data | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 143203.0
num_examples: 3
download_size: 151633
dataset_size: 143203.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zolak/twitter_dataset_81_1713102950 | ---
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: 2804728
num_examples: 6988
download_size: 1405088
dataset_size: 2804728
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
UnderstandLing/oasst1_it_threads | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 11243229
num_examples: 9620
- name: validation
num_bytes: 596552
num_examples: 503
download_size: 6365534
dataset_size: 11839781
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
doushabao4766/ontonotes_zh_ner_knowledge_V3 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: knowledge
dtype: string
splits:
- name: train
num_bytes: 14260725
num_examples: 15724
- name: validation
num_bytes: 4958037
num_examples: 4301
- name: test
num_bytes: 5417233
num_examples: 4346
download_size: 0
dataset_size: 24635995
---
# Dataset Card for "ontonotes_zh_ner_knowledge_V3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kerdel/generative_ai_sample | ---
dataset_info:
features:
- name: name
dtype: string
- name: description
dtype: string
- name: price
dtype: float64
- name: ad
dtype: string
splits:
- name: train
num_bytes: 2026
num_examples: 5
download_size: 6308
dataset_size: 2026
---
# Dataset Card for "generative_ai_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sleoruiz/disc_cla_tercera | ---
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: prediction_agent
dtype: string
- name: annotation
sequence: string
- name: annotation_agent
dtype: string
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
dtype: 'null'
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
struct:
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 15774540
num_examples: 4913
download_size: 8277875
dataset_size: 15774540
---
# Dataset Card for "disc_cla_tercera"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Korakoe/NijiJourney-Prompt-Pairs | ---
license: creativeml-openrail-m
---
# NijiJourney Prompt Pairs
#### A dataset containing txt2img prompt pairs for training on diffusion models
The final goal of this dataset is to create an OpenJourney like model but with NijiJourney images |
pravsels/videos_3b1b_issues | ---
dataset_info:
features:
- name: number
dtype: int64
- name: content
dtype: string
- name: comments
sequence: string
splits:
- name: train
num_bytes: 149126
num_examples: 80
download_size: 37109
dataset_size: 149126
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
artur4002/luna_100stories | ---
language:
- en
license: mit
---
|
open-llm-leaderboard/details_Steelskull__VerA-Etheria-55b | ---
pretty_name: Evaluation run of Steelskull/VerA-Etheria-55b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Steelskull/VerA-Etheria-55b](https://huggingface.co/Steelskull/VerA-Etheria-55b)\
\ 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_Steelskull__VerA-Etheria-55b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-25T17:11:24.913488](https://huggingface.co/datasets/open-llm-leaderboard/details_Steelskull__VerA-Etheria-55b/blob/main/results_2024-01-25T17-11-24.913488.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\": 0.7263827073537332,\n\
\ \"acc_stderr\": 0.029170013986474255,\n \"acc_norm\": 0.7348687053269002,\n\
\ \"acc_norm_stderr\": 0.029706986665856413,\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.016987039266142995,\n \"mc2\": 0.5210415817923857,\n\
\ \"mc2_stderr\": 0.01617919766526897\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.014264122124938218,\n\
\ \"acc_norm\": 0.6424914675767918,\n \"acc_norm_stderr\": 0.014005494275916573\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6434973112925712,\n\
\ \"acc_stderr\": 0.004779872250633708,\n \"acc_norm\": 0.8145787691694881,\n\
\ \"acc_norm_stderr\": 0.0038784463615532884\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.674074074074074,\n\
\ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n\
\ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.0306436070716771,\n\
\ \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.0306436070716771\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\": 0.7849056603773585,\n \"acc_stderr\": 0.02528839450289137,\n\
\ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.02528839450289137\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8680555555555556,\n\
\ \"acc_stderr\": 0.02830096838204443,\n \"acc_norm\": 0.8680555555555556,\n\
\ \"acc_norm_stderr\": 0.02830096838204443\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.58,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.58,\n\
\ \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\
\ \"acc_stderr\": 0.0349610148119118,\n \"acc_norm\": 0.6994219653179191,\n\
\ \"acc_norm_stderr\": 0.0349610148119118\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\
\ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\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\": 0.7531914893617021,\n \"acc_stderr\": 0.02818544130123409,\n\
\ \"acc_norm\": 0.7531914893617021,\n \"acc_norm_stderr\": 0.02818544130123409\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.543859649122807,\n\
\ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.543859649122807,\n\
\ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7448275862068966,\n \"acc_stderr\": 0.03632984052707842,\n\
\ \"acc_norm\": 0.7448275862068966,\n \"acc_norm_stderr\": 0.03632984052707842\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.5529100529100529,\n \"acc_stderr\": 0.025606723995777025,\n \"\
acc_norm\": 0.5529100529100529,\n \"acc_norm_stderr\": 0.025606723995777025\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\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9032258064516129,\n\
\ \"acc_stderr\": 0.016818943416345197,\n \"acc_norm\": 0.9032258064516129,\n\
\ \"acc_norm_stderr\": 0.016818943416345197\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.6157635467980296,\n \"acc_stderr\": 0.034223985656575494,\n\
\ \"acc_norm\": 0.6157635467980296,\n \"acc_norm_stderr\": 0.034223985656575494\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.8363636363636363,\n \"acc_stderr\": 0.028887872395487946,\n\
\ \"acc_norm\": 0.8363636363636363,\n \"acc_norm_stderr\": 0.028887872395487946\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9292929292929293,\n \"acc_stderr\": 0.018263105420199505,\n \"\
acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.018263105420199505\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\
\ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7743589743589744,\n \"acc_stderr\": 0.021193632525148522,\n\
\ \"acc_norm\": 0.7743589743589744,\n \"acc_norm_stderr\": 0.021193632525148522\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \
\ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8361344537815126,\n \"acc_stderr\": 0.02404405494044049,\n \
\ \"acc_norm\": 0.8361344537815126,\n \"acc_norm_stderr\": 0.02404405494044049\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\"\
: 0.9119266055045872,\n \"acc_stderr\": 0.012150743719481685,\n \"\
acc_norm\": 0.9119266055045872,\n \"acc_norm_stderr\": 0.012150743719481685\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6435185185185185,\n \"acc_stderr\": 0.032664783315272714,\n \"\
acc_norm\": 0.6435185185185185,\n \"acc_norm_stderr\": 0.032664783315272714\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9068627450980392,\n \"acc_stderr\": 0.020397853969426994,\n \"\
acc_norm\": 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969426994\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8945147679324894,\n \"acc_stderr\": 0.01999556072375853,\n \
\ \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.01999556072375853\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8071748878923767,\n\
\ \"acc_stderr\": 0.02647824096048937,\n \"acc_norm\": 0.8071748878923767,\n\
\ \"acc_norm_stderr\": 0.02647824096048937\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.03154521672005471,\n\
\ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.03154521672005471\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.859504132231405,\n \"acc_stderr\": 0.031722334260021585,\n \"\
acc_norm\": 0.859504132231405,\n \"acc_norm_stderr\": 0.031722334260021585\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8611111111111112,\n\
\ \"acc_stderr\": 0.03343270062869621,\n \"acc_norm\": 0.8611111111111112,\n\
\ \"acc_norm_stderr\": 0.03343270062869621\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8957055214723927,\n \"acc_stderr\": 0.02401351731943907,\n\
\ \"acc_norm\": 0.8957055214723927,\n \"acc_norm_stderr\": 0.02401351731943907\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5625,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n\
\ \"acc_stderr\": 0.019119892798924978,\n \"acc_norm\": 0.905982905982906,\n\
\ \"acc_norm_stderr\": 0.019119892798924978\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8901660280970626,\n\
\ \"acc_stderr\": 0.011181510503247047,\n \"acc_norm\": 0.8901660280970626,\n\
\ \"acc_norm_stderr\": 0.011181510503247047\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.815028901734104,\n \"acc_stderr\": 0.02090397584208303,\n\
\ \"acc_norm\": 0.815028901734104,\n \"acc_norm_stderr\": 0.02090397584208303\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5318435754189944,\n\
\ \"acc_stderr\": 0.01668855341561221,\n \"acc_norm\": 0.5318435754189944,\n\
\ \"acc_norm_stderr\": 0.01668855341561221\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.826797385620915,\n \"acc_stderr\": 0.02166840025651429,\n\
\ \"acc_norm\": 0.826797385620915,\n \"acc_norm_stderr\": 0.02166840025651429\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8295819935691319,\n\
\ \"acc_stderr\": 0.02135534302826404,\n \"acc_norm\": 0.8295819935691319,\n\
\ \"acc_norm_stderr\": 0.02135534302826404\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.020581466138257117,\n\
\ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.020581466138257117\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6099290780141844,\n \"acc_stderr\": 0.02909767559946393,\n \
\ \"acc_norm\": 0.6099290780141844,\n \"acc_norm_stderr\": 0.02909767559946393\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5730117340286832,\n\
\ \"acc_stderr\": 0.012633353557534416,\n \"acc_norm\": 0.5730117340286832,\n\
\ \"acc_norm_stderr\": 0.012633353557534416\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.02456220431414231,\n\
\ \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.02456220431414231\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7843137254901961,\n \"acc_stderr\": 0.016639319350313264,\n \
\ \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.016639319350313264\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7454545454545455,\n\
\ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.7454545454545455,\n\
\ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8244897959183674,\n \"acc_stderr\": 0.024352800722970015,\n\
\ \"acc_norm\": 0.8244897959183674,\n \"acc_norm_stderr\": 0.024352800722970015\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\
\ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\
\ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466115,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466115\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.02464806896136615,\n\
\ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.02464806896136615\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.016987039266142995,\n \"mc2\": 0.5210415817923857,\n\
\ \"mc2_stderr\": 0.01617919766526897\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224169\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3980288097043215,\n \
\ \"acc_stderr\": 0.013483026939074818\n }\n}\n```"
repo_url: https://huggingface.co/Steelskull/VerA-Etheria-55b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|arc:challenge|25_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|gsm8k|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hellaswag|10_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T17-11-24.913488.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T17-11-24.913488.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- '**/details_harness|winogrande|5_2024-01-25T17-11-24.913488.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-25T17-11-24.913488.parquet'
- config_name: results
data_files:
- split: 2024_01_25T17_11_24.913488
path:
- results_2024-01-25T17-11-24.913488.parquet
- split: latest
path:
- results_2024-01-25T17-11-24.913488.parquet
---
# Dataset Card for Evaluation run of Steelskull/VerA-Etheria-55b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Steelskull/VerA-Etheria-55b](https://huggingface.co/Steelskull/VerA-Etheria-55b) 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_Steelskull__VerA-Etheria-55b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-25T17:11:24.913488](https://huggingface.co/datasets/open-llm-leaderboard/details_Steelskull__VerA-Etheria-55b/blob/main/results_2024-01-25T17-11-24.913488.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": 0.7263827073537332,
"acc_stderr": 0.029170013986474255,
"acc_norm": 0.7348687053269002,
"acc_norm_stderr": 0.029706986665856413,
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142995,
"mc2": 0.5210415817923857,
"mc2_stderr": 0.01617919766526897
},
"harness|arc:challenge|25": {
"acc": 0.6083617747440273,
"acc_stderr": 0.014264122124938218,
"acc_norm": 0.6424914675767918,
"acc_norm_stderr": 0.014005494275916573
},
"harness|hellaswag|10": {
"acc": 0.6434973112925712,
"acc_stderr": 0.004779872250633708,
"acc_norm": 0.8145787691694881,
"acc_norm_stderr": 0.0038784463615532884
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.674074074074074,
"acc_stderr": 0.040491220417025055,
"acc_norm": 0.674074074074074,
"acc_norm_stderr": 0.040491220417025055
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8289473684210527,
"acc_stderr": 0.0306436070716771,
"acc_norm": 0.8289473684210527,
"acc_norm_stderr": 0.0306436070716771
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7849056603773585,
"acc_stderr": 0.02528839450289137,
"acc_norm": 0.7849056603773585,
"acc_norm_stderr": 0.02528839450289137
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8680555555555556,
"acc_stderr": 0.02830096838204443,
"acc_norm": 0.8680555555555556,
"acc_norm_stderr": 0.02830096838204443
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.04960449637488584,
"acc_norm": 0.58,
"acc_norm_stderr": 0.04960449637488584
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.0349610148119118,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.0349610148119118
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.45098039215686275,
"acc_stderr": 0.049512182523962625,
"acc_norm": 0.45098039215686275,
"acc_norm_stderr": 0.049512182523962625
},
"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": 0.7531914893617021,
"acc_stderr": 0.02818544130123409,
"acc_norm": 0.7531914893617021,
"acc_norm_stderr": 0.02818544130123409
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.543859649122807,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.543859649122807,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7448275862068966,
"acc_stderr": 0.03632984052707842,
"acc_norm": 0.7448275862068966,
"acc_norm_stderr": 0.03632984052707842
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.5529100529100529,
"acc_stderr": 0.025606723995777025,
"acc_norm": 0.5529100529100529,
"acc_norm_stderr": 0.025606723995777025
},
"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": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9032258064516129,
"acc_stderr": 0.016818943416345197,
"acc_norm": 0.9032258064516129,
"acc_norm_stderr": 0.016818943416345197
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6157635467980296,
"acc_stderr": 0.034223985656575494,
"acc_norm": 0.6157635467980296,
"acc_norm_stderr": 0.034223985656575494
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8363636363636363,
"acc_stderr": 0.028887872395487946,
"acc_norm": 0.8363636363636363,
"acc_norm_stderr": 0.028887872395487946
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9292929292929293,
"acc_stderr": 0.018263105420199505,
"acc_norm": 0.9292929292929293,
"acc_norm_stderr": 0.018263105420199505
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9740932642487047,
"acc_stderr": 0.01146452335695318,
"acc_norm": 0.9740932642487047,
"acc_norm_stderr": 0.01146452335695318
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7743589743589744,
"acc_stderr": 0.021193632525148522,
"acc_norm": 0.7743589743589744,
"acc_norm_stderr": 0.021193632525148522
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37037037037037035,
"acc_stderr": 0.02944316932303154,
"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.02944316932303154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8361344537815126,
"acc_stderr": 0.02404405494044049,
"acc_norm": 0.8361344537815126,
"acc_norm_stderr": 0.02404405494044049
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.48344370860927155,
"acc_stderr": 0.0408024418562897,
"acc_norm": 0.48344370860927155,
"acc_norm_stderr": 0.0408024418562897
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9119266055045872,
"acc_stderr": 0.012150743719481685,
"acc_norm": 0.9119266055045872,
"acc_norm_stderr": 0.012150743719481685
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6435185185185185,
"acc_stderr": 0.032664783315272714,
"acc_norm": 0.6435185185185185,
"acc_norm_stderr": 0.032664783315272714
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9068627450980392,
"acc_stderr": 0.020397853969426994,
"acc_norm": 0.9068627450980392,
"acc_norm_stderr": 0.020397853969426994
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8945147679324894,
"acc_stderr": 0.01999556072375853,
"acc_norm": 0.8945147679324894,
"acc_norm_stderr": 0.01999556072375853
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8071748878923767,
"acc_stderr": 0.02647824096048937,
"acc_norm": 0.8071748878923767,
"acc_norm_stderr": 0.02647824096048937
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8473282442748091,
"acc_stderr": 0.03154521672005471,
"acc_norm": 0.8473282442748091,
"acc_norm_stderr": 0.03154521672005471
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.859504132231405,
"acc_stderr": 0.031722334260021585,
"acc_norm": 0.859504132231405,
"acc_norm_stderr": 0.031722334260021585
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8611111111111112,
"acc_stderr": 0.03343270062869621,
"acc_norm": 0.8611111111111112,
"acc_norm_stderr": 0.03343270062869621
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8957055214723927,
"acc_stderr": 0.02401351731943907,
"acc_norm": 0.8957055214723927,
"acc_norm_stderr": 0.02401351731943907
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5625,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.5625,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.8349514563106796,
"acc_stderr": 0.03675668832233188,
"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.03675668832233188
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.905982905982906,
"acc_stderr": 0.019119892798924978,
"acc_norm": 0.905982905982906,
"acc_norm_stderr": 0.019119892798924978
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.83,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8901660280970626,
"acc_stderr": 0.011181510503247047,
"acc_norm": 0.8901660280970626,
"acc_norm_stderr": 0.011181510503247047
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.815028901734104,
"acc_stderr": 0.02090397584208303,
"acc_norm": 0.815028901734104,
"acc_norm_stderr": 0.02090397584208303
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.5318435754189944,
"acc_stderr": 0.01668855341561221,
"acc_norm": 0.5318435754189944,
"acc_norm_stderr": 0.01668855341561221
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.826797385620915,
"acc_stderr": 0.02166840025651429,
"acc_norm": 0.826797385620915,
"acc_norm_stderr": 0.02166840025651429
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.8295819935691319,
"acc_stderr": 0.02135534302826404,
"acc_norm": 0.8295819935691319,
"acc_norm_stderr": 0.02135534302826404
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8364197530864198,
"acc_stderr": 0.020581466138257117,
"acc_norm": 0.8364197530864198,
"acc_norm_stderr": 0.020581466138257117
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.6099290780141844,
"acc_stderr": 0.02909767559946393,
"acc_norm": 0.6099290780141844,
"acc_norm_stderr": 0.02909767559946393
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5730117340286832,
"acc_stderr": 0.012633353557534416,
"acc_norm": 0.5730117340286832,
"acc_norm_stderr": 0.012633353557534416
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7941176470588235,
"acc_stderr": 0.02456220431414231,
"acc_norm": 0.7941176470588235,
"acc_norm_stderr": 0.02456220431414231
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7843137254901961,
"acc_stderr": 0.016639319350313264,
"acc_norm": 0.7843137254901961,
"acc_norm_stderr": 0.016639319350313264
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7454545454545455,
"acc_stderr": 0.041723430387053825,
"acc_norm": 0.7454545454545455,
"acc_norm_stderr": 0.041723430387053825
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8244897959183674,
"acc_stderr": 0.024352800722970015,
"acc_norm": 0.8244897959183674,
"acc_norm_stderr": 0.024352800722970015
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8805970149253731,
"acc_stderr": 0.02292879327721974,
"acc_norm": 0.8805970149253731,
"acc_norm_stderr": 0.02292879327721974
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.91,
"acc_stderr": 0.028762349126466115,
"acc_norm": 0.91,
"acc_norm_stderr": 0.028762349126466115
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.038695433234721015,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.038695433234721015
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8830409356725146,
"acc_stderr": 0.02464806896136615,
"acc_norm": 0.8830409356725146,
"acc_norm_stderr": 0.02464806896136615
},
"harness|truthfulqa:mc|0": {
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142995,
"mc2": 0.5210415817923857,
"mc2_stderr": 0.01617919766526897
},
"harness|winogrande|5": {
"acc": 0.7592738752959748,
"acc_stderr": 0.012015559212224169
},
"harness|gsm8k|5": {
"acc": 0.3980288097043215,
"acc_stderr": 0.013483026939074818
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
liuyanchen1015/MULTI_VALUE_cola_for_to_pupose | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 1022
num_examples: 14
- name: test
num_bytes: 980
num_examples: 12
- name: train
num_bytes: 7718
num_examples: 86
download_size: 10270
dataset_size: 9720
---
# Dataset Card for "MULTI_VALUE_cola_for_to_pupose"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GateNLP/broad_twitter_corpus | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: broad-twitter-corpus
pretty_name: Broad Twitter Corpus
---
# Dataset Card for broad_twitter_corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
- **Repository:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
- **Paper:** [http://www.aclweb.org/anthology/C16-1111](http://www.aclweb.org/anthology/C16-1111)
- **Leaderboard:** [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
### Dataset Summary
This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.
See the paper, [Broad Twitter Corpus: A Diverse Named Entity Recognition Resource](http://www.aclweb.org/anthology/C16-1111), for details.
### Supported Tasks and Leaderboards
* Named Entity Recognition
* On PWC: [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
### Languages
English from UK, US, Australia, Canada, Ireland, New Zealand; `bcp47:en`
## Dataset Structure
### Data Instances
Feature |Count
---|---:
Documents |9 551
Tokens |165 739
Person entities |5 271
Location entities |3 114
Organization entities |3 732
### Data Fields
Each tweet contains an ID, a list of tokens, and a list of NER tags
- `id`: a `string` feature.
- `tokens`: a `list` of `strings`
- `ner_tags`: a `list` of class IDs (`int`s) representing the NER class:
```
0: O
1: B-PER
2: I-PER
3: B-ORG
4: I-ORG
5: B-LOC
6: I-LOC
```
### Data Splits
Section|Region|Collection period|Description|Annotators|Tweet count
---|---|---|---|---|---:
A | UK| 2012.01| General collection |Expert| 1000
B |UK |2012.01-02 |Non-directed tweets |Expert |2000
E |Global| 2014.07| Related to MH17 disaster| Crowd & expert |200
F |Stratified |2009-2014| Twitterati |Crowd & expert |2000
G |Stratified| 2011-2014| Mainstream news| Crowd & expert| 2351
H |Non-UK| 2014 |General collection |Crowd & expert |2000
The most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.
**Test**: Section F
**Development**: Section H (the paper says "second half of Section H" but ordinality could be ambiguous, so it all goes in. Bonne chance)
**Training**: everything else
## 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
Creative Commons Attribution 4.0 International (CC BY 4.0)
### Citation Information
```
@inproceedings{derczynski2016broad,
title={Broad twitter corpus: A diverse named entity recognition resource},
author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
pages={1169--1179},
year={2016}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
|
ice518/518 | ---
license: openrail
---
|
aisc-team-a1/synthetic-clinical-notes-finetuning | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 410355114
num_examples: 158114
download_size: 183255388
dataset_size: 410355114
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
arthurmluz/xlsum_data-wiki_gptextsum2_results | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
- name: gen_summary
dtype: string
- name: rouge
struct:
- name: rouge1
dtype: float64
- name: rouge2
dtype: float64
- name: rougeL
dtype: float64
- name: rougeLsum
dtype: float64
- name: bert
struct:
- name: f1
sequence: float64
- name: hashcode
dtype: string
- name: precision
sequence: float64
- name: recall
sequence: float64
- name: moverScore
dtype: float64
splits:
- name: validation
num_bytes: 30175080
num_examples: 7175
download_size: 18538939
dataset_size: 30175080
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "xlsum_data-wiki_gptextsum2_results"
rouge={'rouge1': 0.20406948832826113, 'rouge2': 0.05546401643953366, 'rougeL': 0.12740109757325868, 'rougeLsum': 0.12740109757325868}
Bert={'precision': 0.6510593132607198, 'recall': 0.7254875015963246, 'f1': 0.6859854650165146}
mover = 0.5617656306013571 |
wider_face | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-wider
task_categories:
- object-detection
task_ids:
- face-detection
paperswithcode_id: wider-face-1
pretty_name: WIDER FACE
dataset_info:
features:
- name: image
dtype: image
- name: faces
sequence:
- name: bbox
sequence: float32
length: 4
- name: blur
dtype:
class_label:
names:
'0': clear
'1': normal
'2': heavy
- name: expression
dtype:
class_label:
names:
'0': typical
'1': exaggerate
- name: illumination
dtype:
class_label:
names:
'0': normal
'1': 'exaggerate '
- name: occlusion
dtype:
class_label:
names:
'0': 'no'
'1': partial
'2': heavy
- name: pose
dtype:
class_label:
names:
'0': typical
'1': atypical
- name: invalid
dtype: bool
splits:
- name: train
num_bytes: 12049881
num_examples: 12880
- name: test
num_bytes: 3761103
num_examples: 16097
- name: validation
num_bytes: 2998735
num_examples: 3226
download_size: 3676086479
dataset_size: 18809719
---
# Dataset Card for WIDER FACE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://shuoyang1213.me/WIDERFACE/index.html
- **Repository:**
- **Paper:** [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523)
- **Leaderboard:** http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html
- **Point of Contact:** shuoyang.1213@gmail.com
### Dataset Summary
WIDER FACE dataset is a face detection benchmark dataset, of which images are
selected from the publicly available WIDER dataset. We choose 32,203 images and
label 393,703 faces with a high degree of variability in scale, pose and
occlusion as depicted in the sample images. WIDER FACE dataset is organized
based on 61 event classes. For each event class, we randomly select 40%/10%/50%
data as training, validation and testing sets. We adopt the same evaluation
metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets,
we do not release bounding box ground truth for the test images. Users are
required to submit final prediction files, which we shall proceed to evaluate.
### Supported Tasks and Leaderboards
- `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html).
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its face annotations.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'faces': {
'bbox': [
[178.0, 238.0, 55.0, 73.0],
[248.0, 235.0, 59.0, 73.0],
[363.0, 157.0, 59.0, 73.0],
[468.0, 153.0, 53.0, 72.0],
[629.0, 110.0, 56.0, 81.0],
[745.0, 138.0, 55.0, 77.0]
],
'blur': [2, 2, 2, 2, 2, 2],
'expression': [0, 0, 0, 0, 0, 0],
'illumination': [0, 0, 0, 0, 0, 0],
'occlusion': [1, 2, 1, 2, 1, 2],
'pose': [0, 0, 0, 0, 0, 0],
'invalid': [False, False, False, False, False, False]
}
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `faces`: a dictionary of face attributes for the faces present on the image
- `bbox`: the bounding box of each face (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `blur`: the blur level of each face, with possible values including `clear` (0), `normal` (1) and `heavy`
- `expression`: the facial expression of each face, with possible values including `typical` (0) and `exaggerate` (1)
- `illumination`: the lightning condition of each face, with possible values including `normal` (0) and `exaggerate` (1)
- `occlusion`: the level of occlusion of each face, with possible values including `no` (0), `partial` (1) and `heavy` (2)
- `pose`: the pose of each face, with possible values including `typical` (0) and `atypical` (1)
- `invalid`: whether the image is valid or invalid.
### Data Splits
The data is split into training, validation and testing set. WIDER FACE dataset is organized
based on 61 event classes. For each event class, 40%/10%/50%
data is randomly selected as training, validation and testing sets. The training set contains 12880 images, the validation set 3226 images and test set 16097 images.
## Dataset Creation
### Curation Rationale
The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters,
making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping
with heavy occlusion, small scale, and atypical pose.
### Source Data
#### Initial Data Collection and Normalization
WIDER FACE dataset is a subset of the WIDER dataset.
The images in WIDER were collected in the following three steps: 1) Event categories
were defined and chosen following the Large Scale Ontology for Multimedia (LSCOM) [22], which provides around 1000 concepts relevant to video event analysis. 2) Images
are retrieved using search engines like Google and Bing. For
each category, 1000-3000 images were collected. 3) The
data were cleaned by manually examining all the images
and filtering out images without human face. Then, similar
images in each event category were removed to ensure large
diversity in face appearance. A total of 32203 images are
eventually included in the WIDER FACE dataset.
#### Who are the source language producers?
The images are selected from publicly available WIDER dataset.
### Annotations
#### Annotation process
The curators label the bounding boxes for all
the recognizable faces in the WIDER FACE dataset. The
bounding box is required to tightly contain the forehead,
chin, and cheek.. If a face is occluded, they still label it with a bounding box but with an estimation on the scale of occlusion. Similar to the PASCAL VOC dataset [6], they assign an ’Ignore’ flag to the face
which is very difficult to be recognized due to low resolution and small scale (10 pixels or less). After annotating
the face bounding boxes, they further annotate the following
attributes: pose (typical, atypical) and occlusion level (partial, heavy). Each annotation is labeled by one annotator
and cross-checked by two different people.
#### Who are the annotators?
Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang.
### 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
Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang
### Licensing Information
[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
### Citation Information
```
@inproceedings{yang2016wider,
Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
Title = {WIDER FACE: A Face Detection Benchmark},
Year = {2016}}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
ohsuz/DACON_11200 | ---
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 6350161
num_examples: 11200
download_size: 2056441
dataset_size: 6350161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TuringsSolutions/LosPollosHermanos400AndPFAF400 | ---
license: mit
---
|
jinhybr/rvl_cdip_400_train_val_test | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: letter
1: form
2: email
3: handwritten
4: advertisement
5: scientific report
6: scientific publication
7: specification
8: file folder
9: news article
10: budget
11: invoice
12: presentation
13: questionnaire
14: resume
15: memo
- name: ground_truth
dtype: string
splits:
- name: test
num_bytes: 197669272.0
num_examples: 1600
- name: train
num_bytes: 781258280.0
num_examples: 6400
- name: validation
num_bytes: 191125740.0
num_examples: 1600
download_size: 1101475597
dataset_size: 1170053292.0
---
# Dataset Card for "rvl_cdip_400_train_val_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Format
````
DatasetDict({
test: Dataset({
features: ['image', 'label', 'ground_truth'],
num_rows: 1600
})
train: Dataset({
features: ['image', 'label', 'ground_truth'],
num_rows: 6400
})
validation: Dataset({
features: ['image', 'label', 'ground_truth'],
num_rows: 1600
})
})
```` |
Dahoas/dual-fno-cifar10-32 | ---
dataset_info:
features:
- name: images
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 635009024
num_examples: 50048
download_size: 647528662
dataset_size: 635009024
---
# Dataset Card for "dual-fno-cifar10-32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
benayas/banking_llm_v3 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 21973867
num_examples: 10003
- name: test
num_bytes: 6745410
num_examples: 3080
download_size: 2573335
dataset_size: 28719277
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
EarthnDusk/SD-Convert-1-5 | ---
license: creativeml-openrail-m
language:
- en
tags:
- code
pretty_name: Convert SD 1.5 to Diffusers
size_categories:
- n<1K
---
# **SD 1.5 Model Converter**
<a target="_blank" href="https://colab.research.google.com/github/kieranxsomer/convert-scripts/blob/main/Converter_SD1_5_V2_Duct_TapeVersion.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
**A Colab Notebook To Convert SD 1.5 Checkpoint to Diffusers format**
But a horribly duct taped edition. THIS IS IN ALPHA STAGES, WILL BE PATCHING THE CODE AS I GO ALONG.
♻ - USE ONLY FOR NOW: Converter_SD1_5_V2_Duct_TapeVersion.ipynb
♻ - THIS IN THEORY SHOULD WORK ON VAST/RUNPOD - BUT IT IS UNTESTED, JUST CHANGE YOUR DIRECTORIES AS NEEDED!
RIGHT NOW THE INSTRUCTIONS ARE AS FOLLOWS:
♻ - Install/Clone etc
♻ - Download model - Direct port from Linaqruf.
♻ - Open code panel, replace model details. - don't move after you hit play, it does it really quickly.
♻ - Check file browser, if the model/yourmodelhere looks like a diffusers format you're good to go!
♻ - Write Token + Set up your Repo!
♻ - Upload Diffusers!
---
***Patched from*** : https://colab.research.google.com/github/Linaqruf/sdxl-model-converter/blob/main/sdxl_model_converter.ipynb
***Linaqruf @ Github***: https://github.com/Linaqruf
 [](https://lookup.guru/850007095775723532) [](https://ko-fi.com/linaqruf) <a href="https://saweria.co/linaqruf"><img alt="Saweria" src="https://img.shields.io/badge/Saweria-7B3F00?style=flat&logo=ko-fi&logoColor=white"/></a>
**Please use their main scripts for SDXL HERE:**
| Notebook Name | Description | Link |
| --- | --- | --- |
| [Kohya LoRA Trainer XL](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-trainer-XL.ipynb) | LoRA Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-trainer-XL.ipynb) |
| [Kohya Trainer XL](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer-XL.ipynb) | Native Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-trainer-XL.ipynb) |
SD 1.5 Scripts:
| Notebook Name | Description | Link | V14 |
| --- | --- | --- | --- |
| [Kohya LoRA Dreambooth](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb) | LoRA Training (Dreambooth method) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-LoRA-dreambooth.ipynb) |
| [Kohya LoRA Fine-Tuning](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-finetuner.ipynb) | LoRA Training (Fine-tune method) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-finetuner.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-LoRA-finetuner.ipynb) |
| [Kohya Trainer](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb) | Native Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-trainer.ipynb) |
| [Kohya Dreambooth](https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-dreambooth.ipynb) | Dreambooth Training | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-dreambooth.ipynb) | [](https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/ff701379c65380c967cd956e4e9e8f6349563878/kohya-dreambooth.ipynb) |
Ahoy! you're looking for our Huggingface backup that is again patched from Linaqruf and others?
| Notebook Name | Description | Link |
| --- | --- | --- |
| [Huggingface Backup](https://colab.research.google.com/github/kieranxsomer/HuggingFace_Backup/blob/main/HuggingFace_Backup.ipynb) | backup checkpoints! | [](https://colab.research.google.com/github/kieranxsomer/HuggingFace_Backup/blob/main/HuggingFace_Backup.ipynb)
| [1.5 Conversions](https://github.com/kieranxsomer/convert-scripts/blob/main/Converter_SD1_5_V2_Duct_TapeVersion.ipynb) | Convert to Diffusers! | [](https://github.com/kieranxsomer/convert-scripts/blob/main/Converter_SD1_5_V2_Duct_TapeVersion.ipynb)
## Duskfall/ Earth & Dusk Socials
| Social Network | Link |
| --- | --- |
|Discord|[Invite](https://discord.gg/5t2kYxt7An)
|CivitAi|[Duskfallcrew](https://civitai.com/user/duskfallcrew/)
|Huggingface|[Earth & Dusk](https://huggingface.co/EarthnDusk)
|Ko-Fi| [Dusk's Kofi](https://ko-fi.com/duskfallcrew/) |
JoaoJunior/python_java_dataset_APR | ---
dataset_info:
features:
- name: rem
dtype: string
- name: add
dtype: string
- name: context
dtype: string
- name: meta
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 14814811902
num_examples: 2728295
- name: test
num_bytes: 3704062611
num_examples: 681983
download_size: 5172322839
dataset_size: 18518874513
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "python_java_dataset_APR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_eachadea__vicuna-13b-1.1 | ---
pretty_name: Evaluation run of eachadea/vicuna-13b-1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [eachadea/vicuna-13b-1.1](https://huggingface.co/eachadea/vicuna-13b-1.1) 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_eachadea__vicuna-13b-1.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-14T21:09:04.569052](https://huggingface.co/datasets/open-llm-leaderboard/details_eachadea__vicuna-13b-1.1/blob/main/results_2023-10-14T21-09-04.569052.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\": 0.029677013422818792,\n\
\ \"em_stderr\": 0.0017378324714143493,\n \"f1\": 0.09310612416107406,\n\
\ \"f1_stderr\": 0.002167792401176146,\n \"acc\": 0.4141695683211732,\n\
\ \"acc_stderr\": 0.010019161585538096\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.029677013422818792,\n \"em_stderr\": 0.0017378324714143493,\n\
\ \"f1\": 0.09310612416107406,\n \"f1_stderr\": 0.002167792401176146\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08642911296436695,\n \
\ \"acc_stderr\": 0.00774004433710381\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7419100236779794,\n \"acc_stderr\": 0.012298278833972384\n\
\ }\n}\n```"
repo_url: https://huggingface.co/eachadea/vicuna-13b-1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|arc:challenge|25_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_14T21_09_04.569052
path:
- '**/details_harness|drop|3_2023-10-14T21-09-04.569052.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-14T21-09-04.569052.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_14T21_09_04.569052
path:
- '**/details_harness|gsm8k|5_2023-10-14T21-09-04.569052.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-14T21-09-04.569052.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hellaswag|10_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:54:56.836268.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T18:54:56.836268.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T18:54:56.836268.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_14T21_09_04.569052
path:
- '**/details_harness|winogrande|5_2023-10-14T21-09-04.569052.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-14T21-09-04.569052.parquet'
- config_name: results
data_files:
- split: 2023_07_19T18_54_56.836268
path:
- results_2023-07-19T18:54:56.836268.parquet
- split: 2023_10_14T21_09_04.569052
path:
- results_2023-10-14T21-09-04.569052.parquet
- split: latest
path:
- results_2023-10-14T21-09-04.569052.parquet
---
# Dataset Card for Evaluation run of eachadea/vicuna-13b-1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/eachadea/vicuna-13b-1.1
- **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 [eachadea/vicuna-13b-1.1](https://huggingface.co/eachadea/vicuna-13b-1.1) 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_eachadea__vicuna-13b-1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T21:09:04.569052](https://huggingface.co/datasets/open-llm-leaderboard/details_eachadea__vicuna-13b-1.1/blob/main/results_2023-10-14T21-09-04.569052.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": 0.029677013422818792,
"em_stderr": 0.0017378324714143493,
"f1": 0.09310612416107406,
"f1_stderr": 0.002167792401176146,
"acc": 0.4141695683211732,
"acc_stderr": 0.010019161585538096
},
"harness|drop|3": {
"em": 0.029677013422818792,
"em_stderr": 0.0017378324714143493,
"f1": 0.09310612416107406,
"f1_stderr": 0.002167792401176146
},
"harness|gsm8k|5": {
"acc": 0.08642911296436695,
"acc_stderr": 0.00774004433710381
},
"harness|winogrande|5": {
"acc": 0.7419100236779794,
"acc_stderr": 0.012298278833972384
}
}
```
### 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] |
zpdsherlock/trace-text | ---
license: mit
---
|
CyberHarem/kronshtadt_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kronshtadt/クロンシュタット/喀琅施塔得 (Azur Lane)
This is the dataset of kronshtadt/クロンシュタット/喀琅施塔得 (Azur Lane), containing 90 images and their tags.
The core tags of this character are `blue_eyes, breasts, long_hair, large_breasts, blonde_hair, very_long_hair, bangs, mole, mole_on_breast, parted_bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 90 | 177.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kronshtadt_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 90 | 81.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kronshtadt_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 231 | 173.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kronshtadt_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 90 | 146.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kronshtadt_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 231 | 269.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kronshtadt_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/kronshtadt_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 | 6 |  |  |  |  |  | 1girl, black_bra, black_choker, black_panties, black_pantyhose, cleavage, collarbone, elbow_gloves, looking_at_viewer, solo, bare_shoulders, simple_background, white_background, blush, hair_ribbon, huge_breasts, panties_under_pantyhose, thighs, anchor_choker, covered_navel, hair_flower, lace_bra, lingerie, low_twintails, parted_lips |
| 1 | 17 |  |  |  |  |  | 1girl, cleavage, solo, white_dress, black_gloves, elbow_gloves, black_bra, black_choker, bra_peek, looking_at_viewer, black_pantyhose, hair_flower, standing, fur-trimmed_coat, white_coat, sword, white_flower, closed_mouth, collarbone, holding, simple_background |
| 2 | 24 |  |  |  |  |  | black_bra, 1girl, black_gloves, open_shirt, solo, white_shirt, cleavage, black_skirt, long_sleeves, looking_at_viewer, pencil_skirt, black_pantyhose, collared_shirt, official_alternate_costume, high-waist_skirt, miniskirt, black_belt, collarbone, handcuffs, standing, black_choker, thigh_strap, puffy_sleeves, holding, black_footwear, blush, closed_mouth, dress_shirt, headphones, megaphone, sidelocks, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bra | black_choker | black_panties | black_pantyhose | cleavage | collarbone | elbow_gloves | looking_at_viewer | solo | bare_shoulders | simple_background | white_background | blush | hair_ribbon | huge_breasts | panties_under_pantyhose | thighs | anchor_choker | covered_navel | hair_flower | lace_bra | lingerie | low_twintails | parted_lips | white_dress | black_gloves | bra_peek | standing | fur-trimmed_coat | white_coat | sword | white_flower | closed_mouth | holding | open_shirt | white_shirt | black_skirt | long_sleeves | pencil_skirt | collared_shirt | official_alternate_costume | high-waist_skirt | miniskirt | black_belt | handcuffs | thigh_strap | puffy_sleeves | black_footwear | dress_shirt | headphones | megaphone | sidelocks |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:---------------|:----------------|:------------------|:-----------|:-------------|:---------------|:--------------------|:-------|:-----------------|:--------------------|:-------------------|:--------|:--------------|:---------------|:--------------------------|:---------|:----------------|:----------------|:--------------|:-----------|:-----------|:----------------|:--------------|:--------------|:---------------|:-----------|:-----------|:-------------------|:-------------|:--------|:---------------|:---------------|:----------|:-------------|:--------------|:--------------|:---------------|:---------------|:-----------------|:-----------------------------|:-------------------|:------------|:-------------|:------------|:--------------|:----------------|:-----------------|:--------------|:-------------|:------------|:------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 17 |  |  |  |  |  | X | X | X | | X | X | X | X | X | X | | X | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 2 | 24 |  |  |  |  |  | X | X | X | | X | X | X | | X | X | | X | | X | | | | | | | | | | | | | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
EgilKarlsen/AA_GPTNEO_Finetuned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 213730620.21618995
num_examples: 26057
- name: test
num_bytes: 71246407.07358725
num_examples: 8686
download_size: 392449335
dataset_size: 284977027.2897772
---
# Dataset Card for "AA_GPTNEO_Finetuned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-d42d3c12-7815013 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: Ninh/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Ninh/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
elisachen/uber-trips | ---
license: bsd
---
|
ImperialIndians23/nlp_cw_data_unprocessed_downsampled | ---
dataset_info:
features:
- name: par_id
dtype: string
- name: community
dtype: string
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 716843.2040597015
num_examples: 2382
- name: valid
num_bytes: 616626
num_examples: 2094
download_size: 857136
dataset_size: 1333469.2040597014
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
---
|
dchatca/vn-economic-articles-summary | ---
dataset_info:
features:
- name: Title
dtype: string
- name: Content
dtype: string
- name: Sum-Content
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 10874425.438871473
num_examples: 1020
- name: test
num_bytes: 2729267.5611285265
num_examples: 256
download_size: 6416873
dataset_size: 13603693.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
ricahrd/TzDaCoronel | ---
license: openrail
---
|
luisroque/instruct-python-500k | ---
dataset_info:
features:
- name: score_question
dtype: int16
- name: score_answer
dtype: int16
- name: question
dtype: string
- name: answer
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 987469369
num_examples: 501349
download_size: 550185963
dataset_size: 987469369
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- en
pretty_name: Instruct Python 500k
size_categories:
- 100K<n<1M
---
# Fine-tuning Instruct Stack Overflow Python Q&A
## Transformed Dataset
### Objective
The transformed dataset is designed for fine-tuning LLMs to improve Python coding assistance by focusing on high-quality content from Stack Overflow.
### Structure
- **Question-Answer Pairing**: Questions and answers are paired using the `ParentId` linkage.
- **Quality Focus**: Only top-rated answers for each question are retained.
- **HTML Tag Removal**: All HTML tags in the content are removed.
- **Combined Question Field**: Each question's title and body are merged.
- **Filtering**: Entries with negative scores or those not containing Python code structures are excluded.
Final columns:
- `score_question`
- `score_answer`
- `question`
- `answer`
## Original Dataset
The dataset contains questions and answers from Stack Overflow with the `python` tag, covering the period from August 2, 2008, to October 19, 2016.
## License
All contributions are under the [CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). Attribution is required. The original dataset was posted [here](https://www.kaggle.com/datasets/stackoverflow/pythonquestions).
Keep in touch: [LinkedIn](https://www.linkedin.com/in/luisbrasroque/) |
heliosprime/twitter_dataset_1712957203 | ---
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: 2367
num_examples: 5
download_size: 7570
dataset_size: 2367
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1712957203"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EleutherAI/quirky_authors_alice_easy | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 341886.7603025158
num_examples: 2430
- name: validation
num_bytes: 68034.53475
num_examples: 483
- name: test
num_bytes: 65741.3675
num_examples: 470
download_size: 210208
dataset_size: 475662.6625525158
---
# Dataset Card for "quirky_authors_alice_easy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hltcoe/tdist-msmarco-scores | ---
license: mit
---
# MS MARCO Distillation Scores for Translate-Distill
This repository contains [MS MARCO](https://microsoft.github.io/msmarco/) training
query-passage scores produced by MonoT5 reranker
[`unicamp-dl/mt5-13b-mmarco-100k`](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k) and
[`castorini/monot5-3b-msmarco-10k`](https://huggingface.co/castorini/monot5-3b-msmarco-10k).
Each training query is associated with the top-50 passages retrieved by the [ColBERTv2](https://arxiv.org/abs/2112.01488) model.
Files are gzip compressed and with the naming scheme of `{teacher}-monot5-{msmarco, mmarco}-{qlang}{plang}.jsonl.gz`,
which indicates the teacher reranker that inferenced using `qlang` queries and `plang` passages from MS MARCO.
For languages other than English (eng), we use the translated text provided by mmarco and [neuMarco](https://ir-datasets.com/neumarco.html).
We additionally provide the Persian translation of the MS MARCO training queries since they were not included in either neuMARCO or mMARCO.
You can find the tsv files containing the translation in `msmarco.train.query.fas.tsv.gz`.
## Usage
We recommand downloading the files to incorporate with the training script in the [PLAID-X](https://github.com/hltcoe/ColBERT-X/tree/plaid-x) codebase.
## Citation and Bibtex Info
Please cite the following paper if you use the scores.
```bibtext
@inproceedings{translate-distill,
author = {Eugene Yang and Dawn Lawrie and James Mayfield and Douglas W. Oard and Scott Miller},
title = {Translate-Distill: Learning Cross-Language \ Dense Retrieval by Translation and Distillation},
booktitle = {Proceedings of the 46th European Conference on Information Retrieval (ECIR)},
year = {2024},
url = {https://arxiv.org/abs/2401.04810}
}
```
|
aalexchengg/conll_ipa | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
- name: chunk_tags
sequence:
class_label:
names:
'0': O
'1': B-ADJP
'2': I-ADJP
'3': B-ADVP
'4': I-ADVP
'5': B-CONJP
'6': I-CONJP
'7': B-INTJ
'8': I-INTJ
'9': B-LST
'10': I-LST
'11': B-NP
'12': I-NP
'13': B-PP
'14': I-PP
'15': B-PRT
'16': I-PRT
'17': B-SBAR
'18': I-SBAR
'19': B-UCP
'20': I-UCP
'21': B-VP
'22': I-VP
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: word_ids
sequence: int64
- name: token_type_ids
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 25787811
num_examples: 14041
download_size: 2539396
dataset_size: 25787811
---
# Dataset Card for "conll_ipa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Back-up/law-pdf-demo | ---
dataset_info:
features:
- name: place_of_issuance
dtype: string
- name: gazette_number
dtype: string
- name: sign_number
dtype: string
- name: date_of_gazette
dtype: string
- name: type
dtype: string
- name: signer
dtype: string
- name: promulgation_datetime
dtype: string
- name: 'expiration_date:'
dtype: string
- name: fields
dtype: string
- name: subject
dtype: string
- name: meta_data
struct:
- name: file_name
dtype: string
- name: link_download_pdf_file
dtype: string
- name: local_path
dtype: string
- name: source
dtype: string
splits:
- name: demo
num_bytes: 35888
num_examples: 34
download_size: 25074
dataset_size: 35888
configs:
- config_name: default
data_files:
- split: demo
path: data/demo-*
---
|
varora/HIT | ---
license: other
license_name: max-planck
license_link: https://hit.is.tue.mpg.de/license.html
configs:
- config_name: male
data_files:
- split: train
path: male/train/*.gz
- split: val
path: male/val/*.gz
- split: test
path: male/test/*.gz
- config_name: female
data_files:
- split: train
path: female/train/*.gz
- split: val
path: female/val/*.gz
- split: test
path: female/test/*.gz
tags:
- SMPL
- Tissues
- Medical
- Biomechanics
- Human-Twins
- Digital-Twins
- Mesh
- Bones
- 3D
- Classification
- Occupancy
- MRI
- Segmentation
---
## Dataset Description
- **Homepage:** [https://hit.is.tue.mpg.de/](https://hit.is.tue.mpg.de/)
- **Repository:** [https://github.com/MarilynKeller/HIT](https://github.com/MarilynKeller/HIT)
- **Paper:** [Coming Soon](Coming Soon)
- **Point of Contact:** [Marilyn Keller](marilyn.keller@tuebingen.mpg.de), [Sergi Pujades](sergi.pujades-rocamora@inria.fr), [Vaibhav Arora](vaibhav.arora@inria.fr)
### Dataset Summary
The HIT dataset is a structured dataset of paired observations of body's inner tissues and the body surface. More concretely, it is a dataset of paired full-body volumetric segmented (bones, lean, and adipose tissue) MRI scans and SMPL meshes capturing the body surface shape for male (N=157) and female (N=241) subjects respectively. This is relevant for medicine, sports science, biomechanics, and computer graphics as it can ease the creation of personalized anatomic digital twins that model our bones, lean, and adipose tissue.
Dataset acquistion: We work with scans acquired with a 1.5 T scanner (Magnetom Sonata, Siemens Healthcare) following a standardized protocol for whole body adipose tissue topography mapping. All subjects gave prior informed written consent and the study was approved by the local ethics board. Each scan has around 110 slices, slightly varying depending on the height of the subject. The slice resolution is 256 × 192, with an approximate voxel size of 2 × 2 × 10 mm. These slices are segmented into bones, lean, and adipose tissue by leveraging initial automatic segmentations and manual annotations to train and refine nnUnets with the help of human supervision. For each subject, we then fit the SMPL body mesh to the surface of the segmented MRI in a manner that captures the flattened shape of subjects in their lying positions on belly in the scanner (refer to Sec 3.2 in main paper for further details). Therefore for each subject, we provide the MRI segmented array and the SMPL mesh faces and vertices (in addition to the SMPL parameters).
<img src="extras/hit_dataset.png" alt="alt text" width="300">
### Supported Tasks and Leaderboards
HIT fosters a new direction and therefore there aren't any exisiting Benchmarks. We encourage the use of the dataset to open up new tasks and research directions.
### Languages
[N/A]
## Usage
### Quick use
```angular2html
pip install datasets
```
```angular2html
from datasets import load_dataset
# name in ['male', 'female']
# split in ['train', 'validation', 'test']
male_train = load_dataset("varora/hit", name='male', split='train')
print(male_train.__len__())
print(next(iter(male_train)))
```
### Visualize data
Download `vis_hit_sample.py` from the repo or `git clone https://huggingface.co/datasets/varora/HIT`
```angular2html
pip install datasets, open3d, pyvista
```
#### Visualize mesh and pointcloud
```angular2html
python vis_hit_sample.py --gender male --split test --idx 5 --show_skin
```
<img src="extras/vis_script_output.png" alt="alt text" width="300">
#### Visualize tissue slice
```angular2html
python vis_hit_sample.py --gender male --split test --idx 5 --show_tissue
```
<img src="extras/tissue_slice_frontal.png" alt="alt text" width="300">
## Dataset Structure
The dataset is structured as follows:
```
|- male
|- train
|- 001.gz
|- 002.gz
|- …
|- 00X.gz
|- val
|-
|- …
|- 00X.gz
|- test
|-
|- …
|- 00X.gz
|- female
|- train
|- 001.gz
|- 002.gz
|- …
|- 00X.gz
|- val
|-
|- …
|- 00X.gz
|- test
|-
|- …
|- 00X.gz
```
### Data Instances
Each data instance (male/train/001.gz for example) contains the following:
```
{
'gender': str ['male', 'female'],
'subject_ID': str
'mri_seg': numpy.ndarray (None, 192, 256),
'mri_labels': dict {'NO': 0, 'LT': 1, 'AT': 2, 'VAT': 3, 'BONE': 4},
'body_mask': numpy.ndarray (None, 192, 256),
'bondy_cont_pc': numpy.ndarray (None, 3),
'resolution': numpy.ndarray (N, 3),
'center': numpy.ndarray (N, 3),
'smpl_dict': dict dict_keys(['gender', 'verts_free', 'verts', 'faces', 'pose', 'betas', 'trans'])
}
```
### Data Fields
Each data instance (male/train/001.gz for example) contains the following fields:
- 'gender': "gender of the subject",
- 'subject_ID': "anonymized name of the subject which is also the filename"
- 'mri_seg': "annotated array with the labels 0,1,2,3",
- 'mri_labels': "dictionary of mapping between label integer and name",
- 'body_mask': "binary array for body mask",
- 'body_cont_pc' "extracted point cloud from mri contours"
- 'resolution': "per slice resolution in meters",
- 'center': "per slice center, in pixels",
- 'smpl_dict': dictionary containing all the relevant SMPL parameters of the subject alongwith mesh faces and vertices ('verts': original fit, 'verts_free': compressed fit
### Data Splits
The HIT dataset has 3 splits for each subject type (male, female): train, val, and test.
| | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| male | 126 | 16 | 15 |
| female | 191 | 25 | 25 |
## Dataset Creation
### Curation Rationale
The dataset was created to foster research in biomechanics, computer graphics and Human Digital Twins.
### Source Data
#### Initial Data Collection and Normalization
We work with scans acquired with a 1.5 T scanner (Magnetom Sonata, Siemens Healthcare) following a standardized protocol for whole body adipose tissue topography mapping. All subjects gave prior informed written consent and the study was approved by the local ethics board. Each scan has around 110 slices, slightly varying depending on the height of the subject. The slice resolution is 256 × 192, with an approximate voxel size of 2 × 2 × 10 mm. These slices are segmented into bones, lean, and adipose tissue by leveraging initial automatic segmentations and manual annotations to train and refine nnUnets with the help of human supervision. For each subject, we then fit the SMPL body mesh to the surface of the segmented MRI in a manner that captures the flattened shape of subjects in their lying positions on belly in the scanner (refer to Sec 3.2 in main paper for further details). Therefore for each subject, we provide the MRI segmented array and the SMPL mesh faces and vertices (in addition to the SMPL parameters).
#### Who are the source language producers?
[N/A]
### Annotations
#### Annotation process
Refer to Sec 3 of the paper.
#### Who are the annotators?
Refer to Sec 3 of the paper.
### Personal and Sensitive Information
The dataset uses identity category of gender: male and female. As the dataset intends to foster research in estimating tissues from outer shape which vary subsequently between the genders, the dataset is categorized as such.
## Considerations for Using the Data
### Social Impact of Dataset
Today, many methods can estimate accurate SMPL bodies from images, and this dataset can be used to train models that can infer their internal tissues. As a good estimate of the body composition relates to health risks, HIT dataset could allow the estimation of health risks from a single image of a person. This is valuable as an early diagnostic tool when used with the persons knowledge, but could turn into a risk if it is used without consent.
### Discussion of Biases
[N/A]
### Other Known Limitations
Refer to Sec 3.3 of the paper
## Additional Information
### Dataset Curators
The HIT dataset was curated by [Vaibhav Arora](vaibhav.arora@inria.fr), Abdelmouttaleb Dakri, Jürgen Machann, Sergi Pujades
### Licensing Information
#### Software Copyright License for non-commercial scientific research purposes
Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use the HIT data and software, (the "Data & Software"), including trained models, 3D meshes, images, videos, textures, software, scripts, and animations. By downloading and/or using the Data & Software (including downloading, cloning, installing, and any other use of the corresponding github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Data & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.
#### Ownership/Licensees
The Software and the associated materials has been developed at the Max Planck Institute for Intelligent Systems (hereinafter "MPI"), University of Tübingen, and INRIA. The original skeleton mesh is released with permission of Anatoscope (www.anatoscope.com).
Any copyright or patent right is owned by and proprietary material of the Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (hereinafter “MPG”; MPI and MPG hereinafter collectively “Max-Planck”), hereinafter the “Licensor”.
#### License Grant
Licensor grants you (Licensee) personally a single-user, non-exclusive, non-transferable, free of charge right:
- To install the Data & Software on computers owned, leased or otherwise controlled by you and/or your organization;
- To use the Data & Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects;
Any other use, in particular any use for commercial, pornographic, military, or surveillance, purposes is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes. The Data & Software may not be used to create fake, libelous, misleading, or defamatory content of any kind excluding analyses in peer-reviewed scientific research. The Software may not be reproduced, modified and/or made available in any form to any third party without Max-Planck’s prior written permission.
The Data & Software may not be used for pornographic purposes or to generate pornographic material whether commercial or not. This license also prohibits the use of the Software to train methods/algorithms/neural networks/etc. for commercial, pornographic, military, surveillance, or defamatory use of any kind. By downloading the Data & Software, you agree not to reverse engineer it.
#### No Distribution
The Data & Software and the license herein granted shall not be copied, shared, distributed, re-sold, offered for re-sale, transferred or sub-licensed in whole or in part except that you may make one copy for archive purposes only.
#### Disclaimer of Representations and Warranties
You expressly acknowledge and agree that the Data & Software results from basic research, is provided “AS IS”, may contain errors, and that any use of the Data & Software is at your sole risk. LICENSOR MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE DATA & SOFTWARE, NEITHER EXPRESS NOR IMPLIED, AND THE ABSENCE OF ANY LEGAL OR ACTUAL DEFECTS, WHETHER DISCOVERABLE OR NOT. Specifically, and not to limit the foregoing, licensor makes no representations or warranties (i) regarding the merchantability or fitness for a particular purpose of the Data & Software, (ii) that the use of the Data & Software will not infringe any patents, copyrights or other intellectual property rights of a third party, and (iii) that the use of the Data & Software will not cause any damage of any kind to you or a third party.
#### Limitation of Liability
Because this Data & Software License Agreement qualifies as a donation, according to Section 521 of the German Civil Code (Bürgerliches Gesetzbuch – BGB) Licensor as a donor is liable for intent and gross negligence only. If the Licensor fraudulently conceals a legal or material defect, they are obliged to compensate the Licensee for the resulting damage.
Licensor shall be liable for loss of data only up to the amount of typical recovery costs which would have arisen had proper and regular data backup measures been taken. For the avoidance of doubt Licensor shall be liable in accordance with the German Product Liability Act in the event of product liability. The foregoing applies also to Licensor’s legal representatives or assistants in performance. Any further liability shall be excluded.
Patent claims generated through the usage of the Data & Software cannot be directed towards the copyright holders.
The Data & Software is provided in the state of development the licensor defines. If modified or extended by Licensee, the Licensor makes no claims about the fitness of the Data & Software and is not responsible for any problems such modifications cause.
#### No Maintenance Services
You understand and agree that Licensor is under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Data & Software. Licensor nevertheless reserves the right to update, modify, or discontinue the Data & Software at any time.
Defects of the Data & Software must be notified in writing to the Licensor with a comprehensible description of the error symptoms. The notification of the defect should enable the reproduction of the error. The Licensee is encouraged to communicate any use, results, modification or publication.
#### Publications using the Data & Software
You acknowledge that the Data & Software is a valuable scientific resource and agree to appropriately reference the following paper in any publication making use of the Data & Software.
#### Commercial licensing opportunities
For commercial uses of the Data & Software, please send email to ps-license@tue.mpg.de
This Agreement shall be governed by the laws of the Federal Republic of Germany except for the UN Sales Convention.
### Citation Information
```
@inproceedings{Keller:CVPR:2024,
title = {{HIT}: Estimating Internal Human Implicit Tissues from the Body Surface},
author = {Keller, Marilyn and Arora, Vaibhav and Dakri, Abdelmouttaleb and Chandhok, Shivam and
Machann, Jürgen and Fritsche, Andreas and Black, Michael J. and Pujades, Sergi},
booktitle = {Proceedings IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
month = jun,
year = {2024},
month_numeric = {6}}
```
### Contributions
[N/A]
|
unum-cloud/ann-t2i-1m | ---
license: apache-2.0
task_categories:
- sentence-similarity
pretty_name: Yandex Text-to-Image 1M Vectors Sample for Nearest Neighbors Search
size_categories:
- 1M<n<10M
---
## Dataset Summary
This dataset contains 200-dimensional vectors for 1M images indexed by Yandex and produced by the Se-ResNext-101 model.
### Usage
```
git lfs install
git clone https://huggingface.co/datasets/unum-cloud/ann-t2i-1m
```
### Dataset Structure
The dataset contains three matrices:
- base: `base.1M.fbin` with 1M vectors to construct the index.
- query: `query.public.100K.fbin` with 100K vectors to lookup in the index.
- truth: `groundtruth.public.100K.ibin` with 10x results for every one of the 100K queries.
Use the [ashvardanian/read_matrix.py](https://gist.github.com/ashvardanian/301b0614252941ac8a3137ac72a18892) Gist to parse the files. |
emozilla/c4-validation.00000-of-00008 | ---
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: timestamp[s]
- name: url
dtype: string
splits:
- name: train
num_bytes: 101515791
num_examples: 45576
download_size: 63164985
dataset_size: 101515791
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
flaviosilva/vozflavio | ---
license: openrail
---
|
BangumiBase/ahogirl | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Aho Girl
This is the image base of bangumi Aho Girl, we detected 28 characters, 6663 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 825 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 107 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 763 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 760 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 688 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 259 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 50 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 276 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 44 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 527 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 388 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 115 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 448 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 42 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 293 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 123 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 15 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 190 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 64 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 121 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 74 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 14 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 53 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 44 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 49 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 79 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 10 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 242 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
AdapterOcean/data-standardized_cluster_4_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 9588432
num_examples: 9018
download_size: 3974733
dataset_size: 9588432
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_4_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/tripclick | ---
pretty_name: '`tripclick`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `tripclick`
The `tripclick` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/tripclick#tripclick).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,523,878
This dataset is used by: [`tripclick_train`](https://huggingface.co/datasets/irds/tripclick_train), [`tripclick_train_head`](https://huggingface.co/datasets/irds/tripclick_train_head), [`tripclick_train_head_dctr`](https://huggingface.co/datasets/irds/tripclick_train_head_dctr), [`tripclick_train_hofstaetter-triples`](https://huggingface.co/datasets/irds/tripclick_train_hofstaetter-triples), [`tripclick_train_tail`](https://huggingface.co/datasets/irds/tripclick_train_tail), [`tripclick_train_torso`](https://huggingface.co/datasets/irds/tripclick_train_torso), [`tripclick_val_head_dctr`](https://huggingface.co/datasets/irds/tripclick_val_head_dctr)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/tripclick', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'url': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Rekabsaz2021TripClick,
title={TripClick: The Log Files of a Large Health Web Search Engine},
author={Navid Rekabsaz and Oleg Lesota and Markus Schedl and Jon Brassey and Carsten Eickhoff},
year={2021},
booktitle={SIGIR}
}
```
|
silk-road/Vanilla-chinese-alpaca-luotuo | ---
license: apache-2.0
language:
- zh
pretty_name: f
size_categories:
- 10K<n<100K
---
Vanilla骆驼是骆驼项目在23年3月21日启动的第一个数据集和模型
我们会陆续将更多数据集发布到hf，包括
- [ ] Coco Caption的中文翻译
- [ ] CoQA的中文翻译
- [ ] CNewSum的Embedding数据
- [ ] 增广的开放QA数据
- [ ] WizardLM的中文翻译
如果你也在做这些数据集的筹备，欢迎来联系我们，避免重复花钱。
# 骆驼(Luotuo): 开源中文大语言模型
[https://github.com/LC1332/Luotuo-Chinese-LLM](https://github.com/LC1332/Luotuo-Chinese-LLM)
骆驼(Luotuo)项目是由[冷子昂](https://blairleng.github.io) @ 商汤科技, 陈启源 @ 华中师范大学 以及 李鲁鲁 @ 商汤科技 发起的中文大语言模型开源项目，包含了一系列语言模型。
( 注意: [陈启源](https://qiyuan-chen.github.io/) 正在寻找2024推免导师，欢迎联系 )
骆驼项目**不是**商汤科技的官方产品。
## Citation
Please cite the repo if you use the data or code in this repo.
```
@misc{alpaca,
author={Ziang Leng, Qiyuan Chen and Cheng Li},
title = {Luotuo: An Instruction-following Chinese Language model, LoRA tuning on LLaMA},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LC1332/Luotuo-Chinese-LLM}},
}
```
|
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