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stringlengths 2
42
⌀ | last_modified
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| likes
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| embedding
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---|---|---|---|---|---|---|---|---|---|
Umbrellos/pablobackyardigans | Umbrellos | 2023-12-10T23:41:00Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-10T23:02:08Z | ---
license: openrail
---
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open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2 | open-llm-leaderboard | 2023-12-10T23:06:21Z | 0 | 0 | [
"region:us"
] | null | 2023-12-10T23:05:40Z | ---
pretty_name: Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenBuddy/openbuddy-deepseek-67b-v15.2](https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-10T23:02:54.951377](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2/blob/main/results_2023-12-10T23-02-54.951377.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.714438762465758,\n\
\ \"acc_stderr\": 0.02999619071511929,\n \"acc_norm\": 0.7169995069840682,\n\
\ \"acc_norm_stderr\": 0.030591759435653683,\n \"mc1\": 0.3929008567931457,\n\
\ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.5620230899021985,\n\
\ \"mc2_stderr\": 0.014942776097379195\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6424914675767918,\n \"acc_stderr\": 0.014005494275916573,\n\
\ \"acc_norm\": 0.6860068259385665,\n \"acc_norm_stderr\": 0.0135626912247263\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6717785301732723,\n\
\ \"acc_stderr\": 0.004686062421158153,\n \"acc_norm\": 0.8636725751842262,\n\
\ \"acc_norm_stderr\": 0.0034243464481037295\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6962962962962963,\n\
\ \"acc_stderr\": 0.039725528847851375,\n \"acc_norm\": 0.6962962962962963,\n\
\ \"acc_norm_stderr\": 0.039725528847851375\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7960526315789473,\n \"acc_stderr\": 0.032790004063100515,\n\
\ \"acc_norm\": 0.7960526315789473,\n \"acc_norm_stderr\": 0.032790004063100515\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.81,\n\
\ \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n \
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7660377358490567,\n \"acc_stderr\": 0.02605529690115292,\n\
\ \"acc_norm\": 0.7660377358490567,\n \"acc_norm_stderr\": 0.02605529690115292\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8541666666666666,\n\
\ \"acc_stderr\": 0.029514245964291762,\n \"acc_norm\": 0.8541666666666666,\n\
\ \"acc_norm_stderr\": 0.029514245964291762\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.58,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\"\
: 0.58,\n \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7052023121387283,\n\
\ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n\
\ \"acc_norm_stderr\": 0.03476599607516478\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6808510638297872,\n \"acc_stderr\": 0.03047297336338005,\n\
\ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.03047297336338005\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.543859649122807,\n\
\ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.543859649122807,\n\
\ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6827586206896552,\n \"acc_stderr\": 0.038783523721386215,\n\
\ \"acc_norm\": 0.6827586206896552,\n \"acc_norm_stderr\": 0.038783523721386215\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.5264550264550265,\n \"acc_stderr\": 0.02571523981134675,\n \"\
acc_norm\": 0.5264550264550265,\n \"acc_norm_stderr\": 0.02571523981134675\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5396825396825397,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8290322580645161,\n \"acc_stderr\": 0.021417242936321582,\n \"\
acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.021417242936321582\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5911330049261084,\n \"acc_stderr\": 0.03459058815883233,\n \"\
acc_norm\": 0.5911330049261084,\n \"acc_norm_stderr\": 0.03459058815883233\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\"\
: 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8242424242424242,\n \"acc_stderr\": 0.02972094300622445,\n\
\ \"acc_norm\": 0.8242424242424242,\n \"acc_norm_stderr\": 0.02972094300622445\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8787878787878788,\n \"acc_stderr\": 0.023253157951942088,\n \"\
acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.023253157951942088\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295141,\n\
\ \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295141\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.022421273612923714,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.022421273612923714\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113114,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113114\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\
\ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\
acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9045871559633027,\n \"acc_stderr\": 0.012595899282335801,\n \"\
acc_norm\": 0.9045871559633027,\n \"acc_norm_stderr\": 0.012595899282335801\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6111111111111112,\n \"acc_stderr\": 0.033247089118091176,\n \"\
acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.033247089118091176\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073322,\n \"\
acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073322\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9071729957805907,\n \"acc_stderr\": 0.018889750550956715,\n \
\ \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.018889750550956715\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\
\ \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n\
\ \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\
\ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8347107438016529,\n \"acc_stderr\": 0.03390780612972776,\n \"\
acc_norm\": 0.8347107438016529,\n \"acc_norm_stderr\": 0.03390780612972776\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.803680981595092,\n \"acc_stderr\": 0.031207970394709225,\n\
\ \"acc_norm\": 0.803680981595092,\n \"acc_norm_stderr\": 0.031207970394709225\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5982142857142857,\n\
\ \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n\
\ \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761012,\n\
\ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761012\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\
\ \"acc_stderr\": 0.016534627684311368,\n \"acc_norm\": 0.9316239316239316,\n\
\ \"acc_norm_stderr\": 0.016534627684311368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9042145593869731,\n\
\ \"acc_stderr\": 0.010524031079055845,\n \"acc_norm\": 0.9042145593869731,\n\
\ \"acc_norm_stderr\": 0.010524031079055845\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7803468208092486,\n \"acc_stderr\": 0.02228963885261789,\n\
\ \"acc_norm\": 0.7803468208092486,\n \"acc_norm_stderr\": 0.02228963885261789\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4670391061452514,\n\
\ \"acc_stderr\": 0.016686126653013934,\n \"acc_norm\": 0.4670391061452514,\n\
\ \"acc_norm_stderr\": 0.016686126653013934\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7973856209150327,\n \"acc_stderr\": 0.02301544687798568,\n\
\ \"acc_norm\": 0.7973856209150327,\n \"acc_norm_stderr\": 0.02301544687798568\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7909967845659164,\n\
\ \"acc_stderr\": 0.02309314039837422,\n \"acc_norm\": 0.7909967845659164,\n\
\ \"acc_norm_stderr\": 0.02309314039837422\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.845679012345679,\n \"acc_stderr\": 0.020100830999851,\n\
\ \"acc_norm\": 0.845679012345679,\n \"acc_norm_stderr\": 0.020100830999851\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5354609929078015,\n \"acc_stderr\": 0.029752389657427054,\n \
\ \"acc_norm\": 0.5354609929078015,\n \"acc_norm_stderr\": 0.029752389657427054\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5573663624511083,\n\
\ \"acc_stderr\": 0.012685906538206239,\n \"acc_norm\": 0.5573663624511083,\n\
\ \"acc_norm_stderr\": 0.012685906538206239\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.026799562024887664,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.026799562024887664\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7777777777777778,\n \"acc_stderr\": 0.01681902837573638,\n \
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.01681902837573638\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7918367346938775,\n \"acc_stderr\": 0.025991117672813296,\n\
\ \"acc_norm\": 0.7918367346938775,\n \"acc_norm_stderr\": 0.025991117672813296\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\
\ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\
\ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n\
\ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3929008567931457,\n\
\ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.5620230899021985,\n\
\ \"mc2_stderr\": 0.014942776097379195\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8445146014206788,\n \"acc_stderr\": 0.010184308214775778\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.66868840030326,\n \
\ \"acc_stderr\": 0.012964999679688687\n }\n}\n```"
repo_url: https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|arc:challenge|25_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|gsm8k|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hellaswag|10_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T23-02-54.951377.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- '**/details_harness|winogrande|5_2023-12-10T23-02-54.951377.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-10T23-02-54.951377.parquet'
- config_name: results
data_files:
- split: 2023_12_10T23_02_54.951377
path:
- results_2023-12-10T23-02-54.951377.parquet
- split: latest
path:
- results_2023-12-10T23-02-54.951377.parquet
---
# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15.2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-deepseek-67b-v15.2](https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-10T23:02:54.951377](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2/blob/main/results_2023-12-10T23-02-54.951377.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.714438762465758,
"acc_stderr": 0.02999619071511929,
"acc_norm": 0.7169995069840682,
"acc_norm_stderr": 0.030591759435653683,
"mc1": 0.3929008567931457,
"mc1_stderr": 0.017097248285233065,
"mc2": 0.5620230899021985,
"mc2_stderr": 0.014942776097379195
},
"harness|arc:challenge|25": {
"acc": 0.6424914675767918,
"acc_stderr": 0.014005494275916573,
"acc_norm": 0.6860068259385665,
"acc_norm_stderr": 0.0135626912247263
},
"harness|hellaswag|10": {
"acc": 0.6717785301732723,
"acc_stderr": 0.004686062421158153,
"acc_norm": 0.8636725751842262,
"acc_norm_stderr": 0.0034243464481037295
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6962962962962963,
"acc_stderr": 0.039725528847851375,
"acc_norm": 0.6962962962962963,
"acc_norm_stderr": 0.039725528847851375
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7960526315789473,
"acc_stderr": 0.032790004063100515,
"acc_norm": 0.7960526315789473,
"acc_norm_stderr": 0.032790004063100515
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7660377358490567,
"acc_stderr": 0.02605529690115292,
"acc_norm": 0.7660377358490567,
"acc_norm_stderr": 0.02605529690115292
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8541666666666666,
"acc_stderr": 0.029514245964291762,
"acc_norm": 0.8541666666666666,
"acc_norm_stderr": 0.029514245964291762
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.04960449637488584,
"acc_norm": 0.58,
"acc_norm_stderr": 0.04960449637488584
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7052023121387283,
"acc_stderr": 0.03476599607516478,
"acc_norm": 0.7052023121387283,
"acc_norm_stderr": 0.03476599607516478
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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sdadasfgdfgfdg/xokit0 | sdadasfgdfgfdg | 2023-12-10T23:14:06Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-10T23:13:12Z | ---
license: openrail
---
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arpachat/FashionTextImageSmall | arpachat | 2023-12-10T23:16:38Z | 0 | 0 | [
"region:us"
] | null | 2023-12-10T23:16:35Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1176081.0
num_examples: 250
download_size: 750208
dataset_size: 1176081.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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sartajbhuvaji/Brain-Tumor-Classification | sartajbhuvaji | 2023-12-10T23:36:07Z | 0 | 1 | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"license:mit",
"biology",
"brain tumor",
"MRI",
"tumor",
"region:us"
] | [
"image-classification"
] | 2023-12-10T23:27:32Z | ---
license: mit
task_categories:
- image-classification
tags:
- biology
- brain tumor
- MRI
- tumor
pretty_name: 'Brain Tumor '
size_categories:
- 1K<n<10K
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vahn9995/booksum-stable-diffusion-prompt | vahn9995 | 2023-12-11T00:22:27Z | 0 | 0 | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:bsd-3-clause-clear",
"region:us"
] | [
"summarization"
] | 2023-12-10T23:27:55Z | ---
license: bsd-3-clause-clear
task_categories:
- summarization
language:
- en
pretty_name: Stable Diffusion Book Sum Prompts
size_categories:
- 1K<n<10K
---
This dataset was based off the https://huggingface.co/datasets/kmfoda/booksum dataset. It was made using GPT 3, inferencing it to create a multitude of stable-diffusion friendly prompts by passing in book chapters
from the booksum dataset. This can be utilized in large-language model fine-tuning or training to create a model that outputs stable diffusion prompts. | [
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marcelosantos/marcelo | marcelosantos | 2023-12-10T23:39:43Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-10T23:36:11Z | ---
license: openrail
---
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mitchcardoso/mitchcardoso | mitchcardoso | 2023-12-10T23:49:11Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-10T23:47:51Z | ---
license: openrail
---
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Vinnyyw/Belivoices | Vinnyyw | 2023-12-10T23:49:49Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-10T23:49:00Z | ---
license: openrail
---
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breno30/AlcyrCruz | breno30 | 2023-12-11T00:06:36Z | 0 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-11T00:06:13Z | ---
license: openrail
---
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open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ | open-llm-leaderboard | 2023-12-11T00:15:56Z | 0 | 0 | [
"region:us"
] | null | 2023-12-11T00:15:15Z | ---
pretty_name: Evaluation run of TheBloke/neural-chat-7B-v3-2-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/neural-chat-7B-v3-2-GPTQ](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-11T00:12:21.907526](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ/blob/main/results_2023-12-11T00-12-21.907526.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6058481456466821,\n\
\ \"acc_stderr\": 0.03323160720607251,\n \"acc_norm\": 0.6077924426433228,\n\
\ \"acc_norm_stderr\": 0.033909992378155715,\n \"mc1\": 0.4541003671970624,\n\
\ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.5979099902582387,\n\
\ \"mc2_stderr\": 0.01509977856693472\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6296928327645052,\n \"acc_stderr\": 0.01411129875167495,\n\
\ \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892978\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6360286795459071,\n\
\ \"acc_stderr\": 0.004801572028920794,\n \"acc_norm\": 0.8324039036048596,\n\
\ \"acc_norm_stderr\": 0.003727438786513393\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\
\ \"acc_stderr\": 0.04304979692464241,\n \"acc_norm\": 0.5407407407407407,\n\
\ \"acc_norm_stderr\": 0.04304979692464241\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.02914690474779833,\n\
\ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.02914690474779833\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\
\ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\
\ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.49,\n\
\ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n\
\ \"acc_stderr\": 0.03772446857518027,\n \"acc_norm\": 0.5722543352601156,\n\
\ \"acc_norm_stderr\": 0.03772446857518027\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n\
\ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n\
\ \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.04657047260594964\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3544973544973545,\n \"acc_stderr\": 0.024636830602842,\n \"acc_norm\"\
: 0.3544973544973545,\n \"acc_norm_stderr\": 0.024636830602842\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7419354838709677,\n\
\ \"acc_stderr\": 0.02489246917246283,\n \"acc_norm\": 0.7419354838709677,\n\
\ \"acc_norm_stderr\": 0.02489246917246283\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175007,\n\
\ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175007\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\
: 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n\
\ \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.024890471769938145,\n\
\ \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.024890471769938145\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871923,\n \
\ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871923\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\
\ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7963302752293578,\n \"acc_stderr\": 0.017266742087630783,\n \"\
acc_norm\": 0.7963302752293578,\n \"acc_norm_stderr\": 0.017266742087630783\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977749,\n \"\
acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n\
\ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6564885496183206,\n \"acc_stderr\": 0.041649760719448786,\n\
\ \"acc_norm\": 0.6564885496183206,\n \"acc_norm_stderr\": 0.041649760719448786\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.04414343666854934,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.04414343666854934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6319018404907976,\n \"acc_stderr\": 0.03789213935838396,\n\
\ \"acc_norm\": 0.6319018404907976,\n \"acc_norm_stderr\": 0.03789213935838396\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\
\ \"acc_stderr\": 0.02514093595033544,\n \"acc_norm\": 0.8205128205128205,\n\
\ \"acc_norm_stderr\": 0.02514093595033544\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7841634738186463,\n\
\ \"acc_stderr\": 0.014711684386139953,\n \"acc_norm\": 0.7841634738186463,\n\
\ \"acc_norm_stderr\": 0.014711684386139953\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.02590663263101613,\n\
\ \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.02590663263101613\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39106145251396646,\n\
\ \"acc_stderr\": 0.016320763763808383,\n \"acc_norm\": 0.39106145251396646,\n\
\ \"acc_norm_stderr\": 0.016320763763808383\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n\
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\
\ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\
\ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6728395061728395,\n \"acc_stderr\": 0.02610567386140983,\n\
\ \"acc_norm\": 0.6728395061728395,\n \"acc_norm_stderr\": 0.02610567386140983\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.42907801418439717,\n \"acc_stderr\": 0.029525914302558555,\n \
\ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.029525914302558555\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4165580182529335,\n\
\ \"acc_stderr\": 0.012591153245057392,\n \"acc_norm\": 0.4165580182529335,\n\
\ \"acc_norm_stderr\": 0.012591153245057392\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5625,\n \"acc_stderr\": 0.030134614954403924,\n \
\ \"acc_norm\": 0.5625,\n \"acc_norm_stderr\": 0.030134614954403924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6045751633986928,\n \"acc_stderr\": 0.01978046595477751,\n \
\ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.01978046595477751\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\
\ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\
\ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.029162738410249772,\n\
\ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.029162738410249772\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n\
\ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n\
\ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.03218093795602357,\n\
\ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.03218093795602357\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4541003671970624,\n\
\ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.5979099902582387,\n\
\ \"mc2_stderr\": 0.01509977856693472\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7947908445146015,\n \"acc_stderr\": 0.01135031570746206\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5284306292645944,\n \
\ \"acc_stderr\": 0.013750202076584419\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|arc:challenge|25_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|gsm8k|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hellaswag|10_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T00-12-21.907526.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- '**/details_harness|winogrande|5_2023-12-11T00-12-21.907526.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-11T00-12-21.907526.parquet'
- config_name: results
data_files:
- split: 2023_12_11T00_12_21.907526
path:
- results_2023-12-11T00-12-21.907526.parquet
- split: latest
path:
- results_2023-12-11T00-12-21.907526.parquet
---
# Dataset Card for Evaluation run of TheBloke/neural-chat-7B-v3-2-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/neural-chat-7B-v3-2-GPTQ](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-11T00:12:21.907526](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ/blob/main/results_2023-12-11T00-12-21.907526.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6058481456466821,
"acc_stderr": 0.03323160720607251,
"acc_norm": 0.6077924426433228,
"acc_norm_stderr": 0.033909992378155715,
"mc1": 0.4541003671970624,
"mc1_stderr": 0.017429593091323522,
"mc2": 0.5979099902582387,
"mc2_stderr": 0.01509977856693472
},
"harness|arc:challenge|25": {
"acc": 0.6296928327645052,
"acc_stderr": 0.01411129875167495,
"acc_norm": 0.659556313993174,
"acc_norm_stderr": 0.013847460518892978
},
"harness|hellaswag|10": {
"acc": 0.6360286795459071,
"acc_stderr": 0.004801572028920794,
"acc_norm": 0.8324039036048596,
"acc_norm_stderr": 0.003727438786513393
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5407407407407407,
"acc_stderr": 0.04304979692464241,
"acc_norm": 0.5407407407407407,
"acc_norm_stderr": 0.04304979692464241
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926605,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.660377358490566,
"acc_stderr": 0.02914690474779833,
"acc_norm": 0.660377358490566,
"acc_norm_stderr": 0.02914690474779833
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7152777777777778,
"acc_stderr": 0.037738099906869334,
"acc_norm": 0.7152777777777778,
"acc_norm_stderr": 0.037738099906869334
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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tanzirghumay/425 | tanzirghumay | 2023-12-11T00:21:11Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-11T00:20:25Z | ---
license: apache-2.0
---
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agvidit1/Dataset-TG-HS-HX-Processed | agvidit1 | 2023-12-11T00:41:43Z | 0 | 0 | [
"region:us"
] | null | 2023-12-11T00:41:41Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4582267
num_examples: 35982
download_size: 2878665
dataset_size: 4582267
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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arpachat/FashionTextImageSmall1 | arpachat | 2023-12-11T00:43:43Z | 0 | 0 | [
"region:us"
] | null | 2023-12-11T00:43:22Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 225387304.77
num_examples: 44441
download_size: 269048072
dataset_size: 225387304.77
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1 | open-llm-leaderboard | 2023-12-11T00:53:08Z | 0 | 0 | [
"region:us"
] | null | 2023-12-11T00:52:28Z | ---
pretty_name: Evaluation run of leejunhyeok/MoMo-70B-LoRA-V1.2_1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [leejunhyeok/MoMo-70B-LoRA-V1.2_1](https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-11T00:49:27.690522](https://huggingface.co/datasets/open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1/blob/main/results_2023-12-11T00-49-27.690522.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6982187092414253,\n\
\ \"acc_stderr\": 0.03042209430227255,\n \"acc_norm\": 0.7019460256253881,\n\
\ \"acc_norm_stderr\": 0.03101566845962337,\n \"mc1\": 0.4394124847001224,\n\
\ \"mc1_stderr\": 0.01737452048251371,\n \"mc2\": 0.6140893594618404,\n\
\ \"mc2_stderr\": 0.014827391515273696\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6715017064846417,\n \"acc_stderr\": 0.0137249784655373,\n\
\ \"acc_norm\": 0.7064846416382252,\n \"acc_norm_stderr\": 0.013307250444941115\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6772555267874926,\n\
\ \"acc_stderr\": 0.004665704208339046,\n \"acc_norm\": 0.8639713204540929,\n\
\ \"acc_norm_stderr\": 0.0034211839093201708\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.0315469804508223,\n\
\ \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.0315469804508223\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544064,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544064\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03116489966694862,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03116489966694862\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n\
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.030783736757745653,\n\
\ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.030783736757745653\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n\
\ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\
acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8225806451612904,\n \"acc_stderr\": 0.02173254068932929,\n \"\
acc_norm\": 0.8225806451612904,\n \"acc_norm_stderr\": 0.02173254068932929\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n \"\
acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\"\
: 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.029311188674983137,\n\
\ \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.029311188674983137\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8888888888888888,\n \"acc_stderr\": 0.022390787638216773,\n \"\
acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.022390787638216773\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n\
\ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6948717948717948,\n \"acc_stderr\": 0.023346335293325884,\n\
\ \"acc_norm\": 0.6948717948717948,\n \"acc_norm_stderr\": 0.023346335293325884\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295894,\n \
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295894\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.45695364238410596,\n \"acc_stderr\": 0.04067325174247443,\n \"\
acc_norm\": 0.45695364238410596,\n \"acc_norm_stderr\": 0.04067325174247443\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9009174311926605,\n \"acc_stderr\": 0.01280978008187893,\n \"\
acc_norm\": 0.9009174311926605,\n \"acc_norm_stderr\": 0.01280978008187893\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6064814814814815,\n \"acc_stderr\": 0.03331747876370312,\n \"\
acc_norm\": 0.6064814814814815,\n \"acc_norm_stderr\": 0.03331747876370312\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9068627450980392,\n \"acc_stderr\": 0.020397853969427,\n \"acc_norm\"\
: 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969427\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.8945147679324894,\n \"acc_stderr\": 0.01999556072375854,\n \"\
acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.01999556072375854\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\
\ \"acc_stderr\": 0.027373095500540193,\n \"acc_norm\": 0.7892376681614349,\n\
\ \"acc_norm_stderr\": 0.027373095500540193\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\
\ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035196,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035196\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8220858895705522,\n \"acc_stderr\": 0.03004735765580663,\n\
\ \"acc_norm\": 0.8220858895705522,\n \"acc_norm_stderr\": 0.03004735765580663\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8697318007662835,\n\
\ \"acc_stderr\": 0.012036729568216054,\n \"acc_norm\": 0.8697318007662835,\n\
\ \"acc_norm_stderr\": 0.012036729568216054\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7832369942196532,\n \"acc_stderr\": 0.022183477668412856,\n\
\ \"acc_norm\": 0.7832369942196532,\n \"acc_norm_stderr\": 0.022183477668412856\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6100558659217877,\n\
\ \"acc_stderr\": 0.016312376629213067,\n \"acc_norm\": 0.6100558659217877,\n\
\ \"acc_norm_stderr\": 0.016312376629213067\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7556270096463023,\n\
\ \"acc_stderr\": 0.024406162094668886,\n \"acc_norm\": 0.7556270096463023,\n\
\ \"acc_norm_stderr\": 0.024406162094668886\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.02118589361522518,\n\
\ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.02118589361522518\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5531914893617021,\n \"acc_stderr\": 0.02965823509766691,\n \
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.02965823509766691\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.559973924380704,\n\
\ \"acc_stderr\": 0.012678037478574513,\n \"acc_norm\": 0.559973924380704,\n\
\ \"acc_norm_stderr\": 0.012678037478574513\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103142,\n\
\ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103142\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7630718954248366,\n \"acc_stderr\": 0.017201662169789772,\n \
\ \"acc_norm\": 0.7630718954248366,\n \"acc_norm_stderr\": 0.017201662169789772\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8081632653061225,\n \"acc_stderr\": 0.025206963154225392,\n\
\ \"acc_norm\": 0.8081632653061225,\n \"acc_norm_stderr\": 0.025206963154225392\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.024112678240900798,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.024112678240900798\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.02567934272327692,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.02567934272327692\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4394124847001224,\n\
\ \"mc1_stderr\": 0.01737452048251371,\n \"mc2\": 0.6140893594618404,\n\
\ \"mc2_stderr\": 0.014827391515273696\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8318863456985004,\n \"acc_stderr\": 0.010510336954166723\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5663381349507203,\n \
\ \"acc_stderr\": 0.013650728047064688\n }\n}\n```"
repo_url: https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|arc:challenge|25_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|gsm8k|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hellaswag|10_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T00-49-27.690522.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- '**/details_harness|winogrande|5_2023-12-11T00-49-27.690522.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-11T00-49-27.690522.parquet'
- config_name: results
data_files:
- split: 2023_12_11T00_49_27.690522
path:
- results_2023-12-11T00-49-27.690522.parquet
- split: latest
path:
- results_2023-12-11T00-49-27.690522.parquet
---
# Dataset Card for Evaluation run of leejunhyeok/MoMo-70B-LoRA-V1.2_1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [leejunhyeok/MoMo-70B-LoRA-V1.2_1](https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-11T00:49:27.690522](https://huggingface.co/datasets/open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1/blob/main/results_2023-12-11T00-49-27.690522.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
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}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03 | open-llm-leaderboard | 2023-12-11T01:11:42Z | 0 | 0 | [
"region:us"
] | null | 2023-12-11T01:11:01Z | ---
pretty_name: Evaluation run of perlthoughts/Chupacabra-7B-v2.03
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [perlthoughts/Chupacabra-7B-v2.03](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-11T01:08:10.868540](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03/blob/main/results_2023-12-11T01-08-10.868540.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6311240267277043,\n\
\ \"acc_stderr\": 0.032483219688012585,\n \"acc_norm\": 0.6342658628547596,\n\
\ \"acc_norm_stderr\": 0.033138776879109225,\n \"mc1\": 0.33659730722154224,\n\
\ \"mc1_stderr\": 0.016542412809494887,\n \"mc2\": 0.48533561495565075,\n\
\ \"mc2_stderr\": 0.015259691745766833\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5981228668941979,\n \"acc_stderr\": 0.014327268614578274,\n\
\ \"acc_norm\": 0.6382252559726962,\n \"acc_norm_stderr\": 0.014041957945038076\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6450906193985262,\n\
\ \"acc_stderr\": 0.004775079636567097,\n \"acc_norm\": 0.8473411670981876,\n\
\ \"acc_norm_stderr\": 0.0035892328893065146\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n\
\ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493857,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.037161774375660164,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.037161774375660164\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n\
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7709677419354839,\n \"acc_stderr\": 0.023904914311782658,\n \"\
acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.023904914311782658\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"\
acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.024537591572830503,\n\
\ \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.024537591572830503\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\
acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374294,\n \"\
acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374294\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"\
acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944863,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944863\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615624,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615624\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\
\ \"acc_stderr\": 0.023902325549560417,\n \"acc_norm\": 0.8418803418803419,\n\
\ \"acc_norm_stderr\": 0.023902325549560417\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n\
\ \"acc_stderr\": 0.013740797258579828,\n \"acc_norm\": 0.8199233716475096,\n\
\ \"acc_norm_stderr\": 0.013740797258579828\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3452513966480447,\n\
\ \"acc_stderr\": 0.01590143260893036,\n \"acc_norm\": 0.3452513966480447,\n\
\ \"acc_norm_stderr\": 0.01590143260893036\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.02617390850671858,\n\
\ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.02617390850671858\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.02492200116888633,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.02492200116888633\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\
\ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\
\ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6433823529411765,\n \"acc_stderr\": 0.02909720956841195,\n\
\ \"acc_norm\": 0.6433823529411765,\n \"acc_norm_stderr\": 0.02909720956841195\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6388888888888888,\n \"acc_stderr\": 0.01943177567703731,\n \
\ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.01943177567703731\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\
\ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\
\ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.673469387755102,\n \"acc_stderr\": 0.030021056238440303,\n\
\ \"acc_norm\": 0.673469387755102,\n \"acc_norm_stderr\": 0.030021056238440303\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.33659730722154224,\n\
\ \"mc1_stderr\": 0.016542412809494887,\n \"mc2\": 0.48533561495565075,\n\
\ \"mc2_stderr\": 0.015259691745766833\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8089976322020521,\n \"acc_stderr\": 0.011047808761510432\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.510235026535254,\n \
\ \"acc_stderr\": 0.013769598923012388\n }\n}\n```"
repo_url: https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|arc:challenge|25_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|gsm8k|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hellaswag|10_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T01-08-10.868540.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- '**/details_harness|winogrande|5_2023-12-11T01-08-10.868540.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-11T01-08-10.868540.parquet'
- config_name: results
data_files:
- split: 2023_12_11T01_08_10.868540
path:
- results_2023-12-11T01-08-10.868540.parquet
- split: latest
path:
- results_2023-12-11T01-08-10.868540.parquet
---
# Dataset Card for Evaluation run of perlthoughts/Chupacabra-7B-v2.03
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [perlthoughts/Chupacabra-7B-v2.03](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-11T01:08:10.868540](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03/blob/main/results_2023-12-11T01-08-10.868540.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6311240267277043,
"acc_stderr": 0.032483219688012585,
"acc_norm": 0.6342658628547596,
"acc_norm_stderr": 0.033138776879109225,
"mc1": 0.33659730722154224,
"mc1_stderr": 0.016542412809494887,
"mc2": 0.48533561495565075,
"mc2_stderr": 0.015259691745766833
},
"harness|arc:challenge|25": {
"acc": 0.5981228668941979,
"acc_stderr": 0.014327268614578274,
"acc_norm": 0.6382252559726962,
"acc_norm_stderr": 0.014041957945038076
},
"harness|hellaswag|10": {
"acc": 0.6450906193985262,
"acc_stderr": 0.004775079636567097,
"acc_norm": 0.8473411670981876,
"acc_norm_stderr": 0.0035892328893065146
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.04244633238353227,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.04244633238353227
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6644736842105263,
"acc_stderr": 0.038424985593952694,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.038424985593952694
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.028152837942493857,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.028152837942493857
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.037161774375660164,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.037161774375660164
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416907,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416907
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.04897104952726366,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.04897104952726366
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.8,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5531914893617021,
"acc_stderr": 0.0325005368436584,
"acc_norm": 0.5531914893617021,
"acc_norm_stderr": 0.0325005368436584
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4473684210526316,
"acc_stderr": 0.04677473004491199,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.04677473004491199
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42328042328042326,
"acc_stderr": 0.02544636563440678,
"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.02544636563440678
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677171,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677171
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7709677419354839,
"acc_stderr": 0.023904914311782658,
"acc_norm": 0.7709677419354839,
"acc_norm_stderr": 0.023904914311782658
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7727272727272727,
"acc_stderr": 0.029857515673386417,
"acc_norm": 0.7727272727272727,
"acc_norm_stderr": 0.029857515673386417
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.02381447708659355,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.02381447708659355
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6256410256410256,
"acc_stderr": 0.024537591572830503,
"acc_norm": 0.6256410256410256,
"acc_norm_stderr": 0.024537591572830503
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028593,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028593
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.030283995525884396
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.41721854304635764,
"acc_stderr": 0.040261414976346104,
"acc_norm": 0.41721854304635764,
"acc_norm_stderr": 0.040261414976346104
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8458715596330275,
"acc_stderr": 0.015480826865374294,
"acc_norm": 0.8458715596330275,
"acc_norm_stderr": 0.015480826865374294
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.803921568627451,
"acc_stderr": 0.027865942286639318,
"acc_norm": 0.803921568627451,
"acc_norm_stderr": 0.027865942286639318
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944863,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944863
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
"acc_stderr": 0.030898610882477515,
"acc_norm": 0.695067264573991,
"acc_norm_stderr": 0.030898610882477515
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7404580152671756,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.7404580152671756,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8055555555555556,
"acc_stderr": 0.038260763248848646,
"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.038260763248848646
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7361963190184049,
"acc_stderr": 0.03462419931615624,
"acc_norm": 0.7361963190184049,
"acc_norm_stderr": 0.03462419931615624
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k | open-llm-leaderboard | 2023-12-11T01:15:11Z | 0 | 0 | [
"region:us"
] | null | 2023-12-11T01:14:28Z | ---
pretty_name: Evaluation run of perlthoughts/Chupacabra-7B-v2.03-128k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [perlthoughts/Chupacabra-7B-v2.03-128k](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-11T01:11:36.377069](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k/blob/main/results_2023-12-11T01-11-36.377069.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6308159681070862,\n\
\ \"acc_stderr\": 0.03241075436743821,\n \"acc_norm\": 0.6340939515561961,\n\
\ \"acc_norm_stderr\": 0.03306369804375197,\n \"mc1\": 0.35495716034271724,\n\
\ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5116101369020933,\n\
\ \"mc2_stderr\": 0.015317262236176729\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735565,\n\
\ \"acc_norm\": 0.6467576791808873,\n \"acc_norm_stderr\": 0.013967822714840056\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6435968930491934,\n\
\ \"acc_stderr\": 0.004779574402771385,\n \"acc_norm\": 0.8456482772356104,\n\
\ \"acc_norm_stderr\": 0.0036054721167622923\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.042446332383532265\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.038781398887976104,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.038781398887976104\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.049020713000019756,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.049020713000019756\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\
\ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\
\ \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\
\ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\
\ \"acc_stderr\": 0.04615186962583703,\n \"acc_norm\": 0.40350877192982454,\n\
\ \"acc_norm_stderr\": 0.04615186962583703\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\
acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\
\ \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n\
\ \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047712,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047712\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593556,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593556\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6435897435897436,\n \"acc_stderr\": 0.024283140529467305,\n\
\ \"acc_norm\": 0.6435897435897436,\n \"acc_norm_stderr\": 0.024283140529467305\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114996,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114996\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650154,\n \"\
acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650154\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474086,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474086\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233504,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233504\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516304,\n \"\
acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516304\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8461538461538461,\n\
\ \"acc_stderr\": 0.02363687331748928,\n \"acc_norm\": 0.8461538461538461,\n\
\ \"acc_norm_stderr\": 0.02363687331748928\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\
\ \"acc_stderr\": 0.013702643715368985,\n \"acc_norm\": 0.8212005108556832,\n\
\ \"acc_norm_stderr\": 0.013702643715368985\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31843575418994413,\n\
\ \"acc_stderr\": 0.015581008080360276,\n \"acc_norm\": 0.31843575418994413,\n\
\ \"acc_norm_stderr\": 0.015581008080360276\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.02617390850671858,\n\
\ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.02617390850671858\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.02465968518596728,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.02465968518596728\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873862,\n \
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873862\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4661016949152542,\n\
\ \"acc_stderr\": 0.012740853872949832,\n \"acc_norm\": 0.4661016949152542,\n\
\ \"acc_norm_stderr\": 0.012740853872949832\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681397,\n\
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681397\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6552287581699346,\n \"acc_stderr\": 0.019228322018696644,\n \
\ \"acc_norm\": 0.6552287581699346,\n \"acc_norm_stderr\": 0.019228322018696644\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484375,\n\
\ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484375\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\
\ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\
\ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35495716034271724,\n\
\ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5116101369020933,\n\
\ \"mc2_stderr\": 0.015317262236176729\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8105761641673244,\n \"acc_stderr\": 0.011012790432989248\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5049279757391963,\n \
\ \"acc_stderr\": 0.01377181577547058\n }\n}\n```"
repo_url: https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|arc:challenge|25_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|gsm8k|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hellaswag|10_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T01-11-36.377069.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- '**/details_harness|winogrande|5_2023-12-11T01-11-36.377069.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-11T01-11-36.377069.parquet'
- config_name: results
data_files:
- split: 2023_12_11T01_11_36.377069
path:
- results_2023-12-11T01-11-36.377069.parquet
- split: latest
path:
- results_2023-12-11T01-11-36.377069.parquet
---
# Dataset Card for Evaluation run of perlthoughts/Chupacabra-7B-v2.03-128k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [perlthoughts/Chupacabra-7B-v2.03-128k](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-11T01:11:36.377069](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k/blob/main/results_2023-12-11T01-11-36.377069.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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},
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}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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gsm8k | null | 2023-12-12T13:28:03Z | 222,956 | 135 | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"math-word-problems",
"arxiv:2110.14168",
"region:us"
] | [
"text2text-generation"
] | 2022-04-12T10:22:10Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: gsm8k
pretty_name: Grade School Math 8K
tags:
- math-word-problems
dataset_info:
- config_name: main
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3963202
num_examples: 7473
- name: test
num_bytes: 713732
num_examples: 1319
download_size: 4915944
dataset_size: 4676934
- config_name: socratic
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 5198108
num_examples: 7473
- name: test
num_bytes: 936859
num_examples: 1319
download_size: 6374717
dataset_size: 6134967
---
# Dataset Card for GSM8K
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://openai.com/blog/grade-school-math/
- **Repository:** https://github.com/openai/grade-school-math
- **Paper:** https://arxiv.org/abs/2110.14168
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
- These problems take between 2 and 8 steps to solve.
- Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer.
- A bright middle school student should be able to solve every problem: from the paper, "Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable."
- Solutions are provided in natural language, as opposed to pure math expressions. From the paper: "We believe this is the most generally useful data format, and we expect it to shed light on the properties of large language models’ internal monologues""
### Supported Tasks and Leaderboards
This dataset is generally used to test logic and math in language modelling.
It has been used for many benchmarks, including the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
For the `main` configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)).
```python
{
'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?',
'answer': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72',
}
```
For the `socratic` configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)), and *Socratic sub-questions*.
```python
{
'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?',
'answer': 'How many clips did Natalia sell in May? ** Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nHow many clips did Natalia sell altogether in April and May? ** Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72',
}
```
### Data Fields
The data fields are the same among `main` and `socratic` configurations and their individual splits.
- question: The question string to a grade school math problem.
- answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.
### Data Splits
| name |train|validation|
|--------|----:|---------:|
|main | 7473| 1319|
|socratic| 7473| 1319|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
From the paper, appendix A:
> We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
Surge AI (surgehq.ai)
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
```bibtex
@article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John},
journal={arXiv preprint arXiv:2110.14168},
year={2021}
}
```
### Contributions
Thanks to [@jon-tow](https://github.com/jon-tow) for adding this dataset. | [
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derek-thomas/dataset-creator-reddit-bestofredditorupdates | derek-thomas | 2023-12-17T05:00:56Z | 28,480 | 0 | [
"region:us"
] | null | 2023-11-17T07:44:26Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: score
dtype: int64
- name: date_utc
dtype: timestamp[ns]
- name: title
dtype: string
- name: flair
dtype: string
- name: poster
dtype: string
- name: permalink
dtype: string
- name: updated
dtype: bool
- name: new
dtype: bool
splits:
- name: train
num_bytes: 63816139
num_examples: 10266
download_size: 37194431
dataset_size: 63816139
---
# Dataset Card for "dataset-creator-reddit-bestofredditorupdates"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
--- Generated Part of README Below ---
## Dataset Overview
The goal is to have an open dataset of [r/bestofredditorupdates](https://www.reddit.com/r/bestofredditorupdates/) submissions. I'm leveraging PRAW and the Reddit API to get downloads.
There is a limit of 1000 in an API call and limited search functionality, so this is run daily to get new submissions.
## Creation Details
This dataset was created by [derek-thomas/dataset-creator-reddit-bestofredditorupdates](https://huggingface.co/spaces/derek-thomas/dataset-creator-reddit-bestofredditorupdates)
## Update Frequency
The dataset is updated daily with the most recent update being `2023-12-17 05:00:00 UTC+0000` where we added **12 new rows**.
## Licensing
[Reddit Licensing terms](https://www.redditinc.com/policies/data-api-terms) as accessed on October 25:
[License information]
## Opt-out
To opt-out of this dataset please make a request in the community tab
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MMMU/MMMU | MMMU | 2023-12-16T02:33:30Z | 7,800 | 74 | [
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"materials",
"mechanical_engineering",
"arxiv:2311.16502",
"region:us"
] | [
"question-answering",
"visual-question-answering",
"multiple-choice"
] | 2023-11-27T17:52:01Z | ---
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configs:
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data_files:
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path: Accounting/validation-*
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path: Accounting/test-*
- config_name: Agriculture
data_files:
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path: Agriculture/validation-*
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path: Agriculture/test-*
- config_name: Architecture_and_Engineering
data_files:
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path: Architecture_and_Engineering/test-*
- config_name: Art
data_files:
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path: Art/dev-*
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path: Art/validation-*
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- config_name: Art_Theory
data_files:
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path: Art_Theory/test-*
- config_name: Basic_Medical_Science
data_files:
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- config_name: Biology
data_files:
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path: Biology/validation-*
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- config_name: Chemistry
data_files:
- split: dev
path: Chemistry/dev-*
- split: validation
path: Chemistry/validation-*
- split: test
path: Chemistry/test-*
- config_name: Clinical_Medicine
data_files:
- split: dev
path: Clinical_Medicine/dev-*
- split: validation
path: Clinical_Medicine/validation-*
- split: test
path: Clinical_Medicine/test-*
- config_name: Computer_Science
data_files:
- split: dev
path: Computer_Science/dev-*
- split: validation
path: Computer_Science/validation-*
- split: test
path: Computer_Science/test-*
- config_name: Design
data_files:
- split: dev
path: Design/dev-*
- split: validation
path: Design/validation-*
- split: test
path: Design/test-*
- config_name: Diagnostics_and_Laboratory_Medicine
data_files:
- split: dev
path: Diagnostics_and_Laboratory_Medicine/dev-*
- split: validation
path: Diagnostics_and_Laboratory_Medicine/validation-*
- split: test
path: Diagnostics_and_Laboratory_Medicine/test-*
- config_name: Economics
data_files:
- split: dev
path: Economics/dev-*
- split: validation
path: Economics/validation-*
- split: test
path: Economics/test-*
- config_name: Electronics
data_files:
- split: dev
path: Electronics/dev-*
- split: validation
path: Electronics/validation-*
- split: test
path: Electronics/test-*
- config_name: Energy_and_Power
data_files:
- split: dev
path: Energy_and_Power/dev-*
- split: validation
path: Energy_and_Power/validation-*
- split: test
path: Energy_and_Power/test-*
- config_name: Finance
data_files:
- split: dev
path: Finance/dev-*
- split: validation
path: Finance/validation-*
- split: test
path: Finance/test-*
- config_name: Geography
data_files:
- split: dev
path: Geography/dev-*
- split: validation
path: Geography/validation-*
- split: test
path: Geography/test-*
- config_name: History
data_files:
- split: dev
path: History/dev-*
- split: validation
path: History/validation-*
- split: test
path: History/test-*
- config_name: Literature
data_files:
- split: dev
path: Literature/dev-*
- split: validation
path: Literature/validation-*
- split: test
path: Literature/test-*
- config_name: Manage
data_files:
- split: dev
path: Manage/dev-*
- split: validation
path: Manage/validation-*
- split: test
path: Manage/test-*
- config_name: Marketing
data_files:
- split: dev
path: Marketing/dev-*
- split: validation
path: Marketing/validation-*
- split: test
path: Marketing/test-*
- config_name: Materials
data_files:
- split: dev
path: Materials/dev-*
- split: validation
path: Materials/validation-*
- split: test
path: Materials/test-*
- config_name: Math
data_files:
- split: dev
path: Math/dev-*
- split: validation
path: Math/validation-*
- split: test
path: Math/test-*
- config_name: Mechanical_Engineering
data_files:
- split: dev
path: Mechanical_Engineering/dev-*
- split: validation
path: Mechanical_Engineering/validation-*
- split: test
path: Mechanical_Engineering/test-*
- config_name: Music
data_files:
- split: dev
path: Music/dev-*
- split: validation
path: Music/validation-*
- split: test
path: Music/test-*
- config_name: Pharmacy
data_files:
- split: dev
path: Pharmacy/dev-*
- split: validation
path: Pharmacy/validation-*
- split: test
path: Pharmacy/test-*
- config_name: Physics
data_files:
- split: dev
path: Physics/dev-*
- split: validation
path: Physics/validation-*
- split: test
path: Physics/test-*
- config_name: Psychology
data_files:
- split: dev
path: Psychology/dev-*
- split: validation
path: Psychology/validation-*
- split: test
path: Psychology/test-*
- config_name: Public_Health
data_files:
- split: dev
path: Public_Health/dev-*
- split: validation
path: Public_Health/validation-*
- split: test
path: Public_Health/test-*
- config_name: Sociology
data_files:
- split: dev
path: Sociology/dev-*
- split: validation
path: Sociology/validation-*
- split: test
path: Sociology/test-*
tags:
- biology
- medical
- finance
- chemistry
- music
- art
- art_theory
- design
- music
- business
- accounting
- economics
- finance
- manage
- marketing
- health
- medicine
- basic_medical_science
- clinical
- pharmacy
- public_health
- humanities
- social_science
- history
- literature
- sociology
- psychology
- science
- biology
- chemistry
- geography
- math
- physics
- engineering
- agriculture
- architecture
- computer_science
- electronics
- energy_and_power
- materials
- mechanical_engineering
---
# MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)
[**🌐 Homepage**](https://mmmu-benchmark.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/MMMU/MMMU/) | [**🤗 Paper**](https://huggingface.co/papers/2311.16502) | [**📖 arXiv**](https://arxiv.org/abs/2311.16502) | [**GitHub**](https://github.com/MMMU-Benchmark/MMMU)
## 🔔News
- **🔥[2023-12-04]: Our evaluation server for test set is now availble on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). We welcome all submissions and look forward to your participation! 😆**
## Dataset Details
### Dataset Description
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes **11.5K meticulously collected multimodal questions** from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span **30 subjects** and **183 subfields**, comprising **30 highly heterogeneous image types**, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI).
🎯 **We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.**
The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the **test set** on **[EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview)**.

### Dataset Creation
MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning.
The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials.
- **Content:** The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects.
- **Image Types:** The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.

## 🏆 Mini-Leaderboard
We show a mini-leaderboard here and please find more information in our paper or [**homepage**](https://mmmu-benchmark.github.io/).
| Model | Val (900) | Test (10.5K) |
|----------------------------|:---------:|:------------:|
| Gemini Ultra* | **59.4** | - |
| GPT-4V(ision) (Playground) | 56.8 | **55.7** |
| Gemini Pro* | 47.9 | - |
| Qwen-VL-PLUS* | 45.2 | 40.8 |
| BLIP-2 FLAN-T5-XXL | 35.4 | 34.0 |
| InstructBLIP-T5-XXL | 35.7 | 33.8 |
| LLaVA-1.5-13B | 36.4 | 33.6 |
| Qwen-VL-7B | 35.9 | 32.9 |
| mPLUG-OWL2* | 32.7 | 32.1 |
| BLIP-2 FLAN-T5-XL | 34.4 | 31.0 |
| InstructBLIP-T5-XL | 32.9 | 30.6 |
| SPHINX* | 32.9 | - |
| Gemini Nano2* | 32.6 | - |
| CogVLM | 32.1 | 30.1 |
| Otter | 32.2 | 29.1 |
| LLaMA-Adapter2-7B | 29.8 | 27.7 |
| MiniGPT4-Vicuna-13B | 26.8 | 27.6 |
| Fuyu-8B | 27.9 | 27.4 |
| Kosmos2 | 24.4 | 26.6 |
| OpenFlamingo2-9B | 28.7 | 26.3 |
| Frequent Choice | 22.1 | 23.9 |
| Random Choice | 26.8 | 25.8 |
*: results provided by the authors.
## Limitations
Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases.
And the focus on college-level subjects might not fully be a sufficient test for Expert AGI.
However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities.
In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI.
## Disclaimers
The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.
Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed.
## Contact
- Xiang Yue: [email protected]
- Yu Su: [email protected]
- Wenhu Chen: [email protected]
## Citation
**BibTeX:**
```bibtex
@article{yue2023mmmu,
title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI},
author={Xiang Yue and Yuansheng Ni and Kai Zhang and Tianyu Zheng and Ruoqi Liu and Ge Zhang and Samuel Stevens and Dongfu Jiang and Weiming Ren and Yuxuan Sun and Cong Wei and Botao Yu and Ruibin Yuan and Renliang Sun and Ming Yin and Boyuan Zheng and Zhenzhu Yang and Yibo Liu and Wenhao Huang and Huan Sun and Yu Su and Wenhu Chen},
journal={arXiv preprint arXiv:2311.16502},
year={2023},
}
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sboughorbel/mmlu_arabic | sboughorbel | 2023-12-17T17:38:06Z | 4,654 | 0 | [
"region:us"
] | null | 2023-11-23T09:48:43Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: correct_answer
dtype: string
splits:
- name: validation
num_bytes: 213556
num_examples: 456
- name: test
num_bytes: 19534326
num_examples: 28084
download_size: 27157379
dataset_size: 19747882
---
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DFKI-SLT/brat | DFKI-SLT | 2023-12-11T09:54:08Z | 3,197 | 2 | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:found",
"region:us"
] | [
"token-classification"
] | 2022-05-10T06:13:33Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
license: []
task_categories:
- token-classification
task_ids:
- parsing
---
# Information Card for Brat
## Table of Contents
- [Description](#description)
- [Summary](#summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Usage](#usage)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Description
- **Homepage:** https://brat.nlplab.org
- **Paper:** https://aclanthology.org/E12-2021/
- **Leaderboard:** \[Needs More Information\]
- **Point of Contact:** \[Needs More Information\]
### Summary
Brat is an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annota- tion for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. brat is designed in particular for structured annotation, where the notes are not free form text but have a fixed form that can be automatically processed and interpreted by a computer.
## Dataset Structure
Dataset annotated with brat format is processed using this script. Annotations created in brat are stored on disk in a standoff format: annotations are stored separately from the annotated document text, which is never modified by the tool. For each text document in the system, there is a corresponding annotation file. The two are associated by the file naming convention that their base name (file name without suffix) is the same: for example, the file DOC-1000.ann contains annotations for the file DOC-1000.txt. More information can be found [here](https://brat.nlplab.org/standoff.html).
### Data Instances
```
{
"context": ''<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n<Document xmlns:gate="http://www.gat...'
"file_name": "A01"
"spans": {
'id': ['T1', 'T2', 'T4', 'T5', 'T6', 'T3', 'T7', 'T8', 'T9', 'T10', 'T11', 'T12',...]
'type': ['background_claim', 'background_claim', 'background_claim', 'own_claim',...]
'locations': [{'start': [2417], 'end': [2522]}, {'start': [2524], 'end': [2640]},...]
'text': ['complicated 3D character models...', 'The range of breathtaking realistic...', ...]
}
"relations": {
'id': ['R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9', 'R10', 'R11', 'R12',...]
'type': ['supports', 'supports', 'supports', 'supports', 'contradicts', 'contradicts',...]
'arguments': [{'type': ['Arg1', 'Arg2'], 'target': ['T4', 'T5']},...]
}
"equivalence_relations": {'type': [], 'targets': []},
"events": {'id': [], 'type': [], 'trigger': [], 'arguments': []},
"attributions": {'id': [], 'type': [], 'target': [], 'value': []},
"normalizations": {'id': [], 'type': [], 'target': [], 'resource_id': [], 'entity_id': []},
"notes": {'id': [], 'type': [], 'target': [], 'note': []},
}
```
### Data Fields
- `context` (`str`): the textual content of the data file
- `file_name` (`str`): the name of the data / annotation file without extension
- `spans` (`dict`): span annotations of the `context` string
- `id` (`str`): the id of the span, starts with `T`
- `type` (`str`): the label of the span
- `locations` (`list`): the indices indicating the span's locations (multiple because of fragments), consisting of `dict`s with
- `start` (`list` of `int`): the indices indicating the inclusive character start positions of the span fragments
- `end` (`list` of `int`): the indices indicating the exclusive character end positions of the span fragments
- `text` (`list` of `str`): the texts of the span fragments
- `relations`: a sequence of relations between elements of `spans`
- `id` (`str`): the id of the relation, starts with `R`
- `type` (`str`): the label of the relation
- `arguments` (`list` of `dict`): the spans related to the relation, consisting of `dict`s with
- `type` (`list` of `str`): the argument roles of the spans in the relation, either `Arg1` or `Arg2`
- `target` (`list` of `str`): the spans which are the arguments of the relation
- `equivalence_relations`: contains `type` and `target` (more information needed)
- `events`: contains `id`, `type`, `trigger`, and `arguments` (more information needed)
- `attributions` (`dict`): attribute annotations of any other annotation
- `id` (`str`): the instance id of the attribution
- `type` (`str`): the type of the attribution
- `target` (`str`): the id of the annotation to which the attribution is for
- `value` (`str`): the attribution's value or mark
- `normalizations` (`dict`): the unique identification of the real-world entities referred to by specific text expressions
- `id` (`str`): the instance id of the normalized entity
- `type`(`str`): the type of the normalized entity
- `target` (`str`): the id of the annotation to which the normalized entity is for
- `resource_id` (`str`): the associated resource to the normalized entity
- `entity_id` (`str`): the instance id of normalized entity
- `notes` (`dict`): a freeform text, added to the annotation
- `id` (`str`): the instance id of the note
- `type` (`str`): the type of note
- `target` (`str`): the id of the related annotation
- `note` (`str`): the text body of the note
### Usage
The `brat` dataset script can be used by calling `load_dataset()` method and passing any arguments that are accepted by the `BratConfig` (which is a special [BuilderConfig](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.BuilderConfig)). It requires at least the `url` argument. The full list of arguments is as follows:
- `url` (`str`): the url of the dataset which should point to either a zip file or a directory containing the Brat data (`*.txt`) and annotation (`*.ann`) files
- `description` (`str`, optional): the description of the dataset
- `citation` (`str`, optional): the citation of the dataset
- `homepage` (`str`, optional): the homepage of the dataset
- `split_paths` (`dict`, optional): a mapping of (arbitrary) split names to subdirectories or lists of files (without extension), e.g. `{"train": "path/to/train_directory", "test": "path/to/test_director"}` or `{"train": ["path/to/train_file1", "path/to/train_file2"]}`. In both cases (subdirectory paths or file paths), the paths are relative to the url. If `split_paths` is not provided, the dataset will be loaded from the root directory and all direct subfolders will be considered as splits.
- `file_name_blacklist` (`list`, optional): a list of file names (without extension) that should be ignored, e.g. `["A28"]`. This is useful if the dataset contains files that are not valid brat files.
Important: Using the `data_dir` parameter of the `load_dataset()` method overrides the `url` parameter of the `BratConfig`.
We provide an example of [SciArg](https://aclanthology.org/W18-5206.pdf) dataset below:
```python
from datasets import load_dataset
kwargs = {
"description" :
"""This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
scientific writing.""",
"citation" :
"""@inproceedings{lauscher2018b,
title = {An argument-annotated corpus of scientific publications},
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
publisher = {Association for Computational Linguistics},
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
address = {Brussels, Belgium},
year = {2018},
pages = {40–46}
}""",
"homepage": "https://github.com/anlausch/ArguminSci",
"url": "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip",
"split_paths": {
"train": "compiled_corpus",
},
"file_name_blacklist": ['A28'],
}
dataset = load_dataset('dfki-nlp/brat', **kwargs)
```
## Additional Information
### Licensing Information
\[Needs More Information\]
### Citation Information
```
@inproceedings{stenetorp-etal-2012-brat,
title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation",
author = "Stenetorp, Pontus and
Pyysalo, Sampo and
Topi{\'c}, Goran and
Ohta, Tomoko and
Ananiadou, Sophia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the Demonstrations at the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2012",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E12-2021",
pages = "102--107",
}
```
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Tianduo/gsm8k-split | Tianduo | 2023-12-12T07:43:33Z | 2,654 | 1 | [
"region:us"
] | null | 2023-12-12T07:01:51Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3559960
num_examples: 6705
- name: dev
num_bytes: 403242
num_examples: 768
- name: test
num_bytes: 713732
num_examples: 1319
download_size: 0
dataset_size: 4676934
---
# Dataset Card for "gsm8k-processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mteb/results | mteb | 2023-12-15T20:14:19Z | 2,236 | 5 | [
"benchmark:mteb",
"region:us"
] | null | 2022-09-01T14:15:23Z | ---
benchmark: mteb
type: evaluation
submission_name: MTEB
--- | [
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geraldOslo/prompt_answer_dataset_for_norwegian_radiation_protection_in_dentistry | geraldOslo | 2023-12-17T09:39:22Z | 1,922 | 0 | [
"size_categories:1K<n<10K",
"language:no",
"license:cc-by-2.0",
"dentistry",
"physics",
"radiation protection",
"doi:10.57967/hf/1291",
"region:us"
] | null | 2023-10-30T08:56:56Z | ---
license: cc-by-2.0
language:
- 'no'
tags:
- dentistry
- physics
- radiation protection
pretty_name: Question/answer connected to radiation protection in dentistry
size_categories:
- 1K<n<10K
configs:
- config_name: tab
data_files: "data.csv"
sep: ";"
---
# This dataset is discontinoued because the name is to long
There are problerms loading this dataset in some systems because the path of the caching system is far to long. I cannot delete it because it has a DOI. The dataset will be developed further here: [geraldOslo/RadProtDataSet](https://huggingface.co/datasets/geraldOslo/RadProtDataSet)
# Contents
## Overview
This dataset comprises prompt/answer pairs related to the curriculum for Norwegian dentistry and dental hygiene students, specifically focusing on the subjects of radiation physics, radiation protection, and radiological technology.
## Data source
The prompt/answer pairs in this dataset were generated using commercially available Large Language Models (LLMs), including OpenAI GPT-4 and Anthropic Claude 2. These pairs were generated based on the analysis of documents provided as input to these LLMs.
Following this, some irrelevant pairs were deleted, some were edited for accuracy and clarity, and all pairs were proofread for errors.
### Source documents
The dataset was created using a variety of source documents, primarily encompassing:
- [Lov om strålevern og bruk av stråling (strålevernloven) (2000)](https://lovdata.no/dokument/SF/forskrift/2016-12-16-1659)
- [Forskrift om strålevern og bruk av stråling (strålevernforskriften) (2016)](https://lovdata.no/dokument/SF/forskrift/2016-12-16-1659)
- [DSA: Medisinsk strålebruk (web page) (2023)](https://dsa.no/medisinsk-stralebruk)
- [DSA: Veileder 14](https://dsa.no/publikasjoner/veileder-14-veileder-om-stralebruk-innen-odontologi/Veileder_14_odontologi.pdf)
- [DSA: StrålevernRapport • 2014:2 Strålebruk i Norge](https://dsa.no/publikasjoner/stralevernrapport-2-2014-stralebruk-i-norge/StralevernRapport_02-2014_Str%C3%A5lebruk%20i%20Norge.pdf)
- [DSA: StrålevernRapport 2015:12 Stråledoser til befolkningen](https://dsa.no/publikasjoner/stralevernrapport-12-2015-straledoser-til-befolkningen/StralevernRapport_12-15_Str%C3%A5ledoser_til_befolkningen-.pdf)
- [DSA: Veileder til forskrift om strålevern og bruk av stråling Veileder Nummer 5 Revidert mai 2023](https://dsa.no/publikasjoner/_/attachment/inline/70e8470f-6c36-46fc-9e97-c27298859d66:22ab78bd659798c58cc3ce55c07dbb9aad9b44a0/Veileder%205_rev-mai2023.pdf)
- [Gerald Torgersen: Strålingsfysikk, strålevern og radiologisk teknologi for tannpleie- og tannlegestudenter (online course) (2023)](https://uio.instructure.com/courses/19845)
- Own teaching material and notes
DSA is the The Norwegian Radiation and Nuclear Safety Authority
# Purpose
The dataset is generated for fine-tuning of open source LLMs.
# Format
The dataset is a UTF-8 formatted ";"-separated csv-file. There are two columns: prompt, prediction
# Warning
The dataset is provided for use on own responsibility. Please give feedback if you find a serious error.
# Todo
- add more relevant prompt/response pairs
- further proofreading and adjustments | [
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0.3789130747318268,
0.4953896701335907,
0.2088727355003357,
0.8342626690864563,
-0.2958158850669861,
0.198996901512146,
0.007877474650740623,
0.17015278339385986,
-0.02227073907852173,
-0.13509823381900787,
-0.6330263018608093,
-0.1762610822916031,
-0.35791853070259094,
0.11117208003997803
] |
qgyd2021/few_shot_intent_sft | qgyd2021 | 2023-12-13T02:21:00Z | 1,798 | 9 | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100M<n<1B",
"language:zh",
"language:en",
"license:apache-2.0",
"few-shot",
"intent",
"arxiv:2003.04807",
"arxiv:1709.10217",
"arxiv:1509.01626",
"arxiv:2307.09705",
"region:us"
] | [
"text-classification",
"text-generation",
"text2text-generation"
] | 2023-09-22T11:26:09Z | ---
license: apache-2.0
task_categories:
- text-classification
- text-generation
- text2text-generation
language:
- zh
- en
tags:
- few-shot
- intent
size_categories:
- 100M<n<1B
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: not_applicable
dtype: bool
- name: intent
dtype: string
- name: intent_version
dtype: string
- name: n_way
dtype: int32
- name: n_shot
dtype: int32
- name: description
dtype: string
splits:
- name: train
num_bytes: 22484898
num_examples: 22080
- name: test
num_bytes: 1853817
num_examples: 2477
download_size: 7816475
dataset_size: 24338715
---
## 小样本意图识别指令数据集
收集了意图识别的数据集, 将其制作成 prompt, 用于 few-shot 的意图识别 LLM 研究.
编写 prompt 模板需要想像力, 你可以在 community 中交流你的想法.
`{dataset_name}_prompt` 子集是从其对应的 `{dataset_name}` 数据集和 `{dataset_name}_template` 子集动态生成的, 因此每一次的结果都会不一样.
提示: 由于训练时 prompt 的长度可能超出最大限制而被 truncate, 因此尽量把 prompt 设计成即使被 truncate 也仍然可以用于 GPT 训练.
[提示工程指南](https://www.promptingguide.ai/zh/techniques/cot)
### 样本示例
<details>
<summary>train subset prompt 示例: (intent: Is it safe to go to the gym indoors if I'm vaccinated?)</summary>
<pre><code>intent recognition.<br>
Examples:
------------
text: will i be okay on the gym
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: I want to go and exercise at the gym, indoors, but I don't know if it's safe?
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: I worry I will catch Covid from the Gym even though I have been vaccinated?
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: What does the fda think about the covid 19 vaccine?
intent: Is the vaccine FDA approved?
------------
text: it's never safe in a gym there are always bacteria everywhere
intent: Is it safe to go to the gym indoors if I'm vaccinated?
------------
text: who is the difference between FDA authorization and approval?
intent: Is the vaccine FDA approved?
------------
text: would the vaccine FDA be approved
intent: Is the vaccine FDA approved?
------------
text: If I had my vaccine, is it safe to go to the indoor gym?
intent:
</code></pre>
</details>
<details>
<summary>train subset prompt 示例: (intent: 考虑一下)</summary>
<pre><code>电销场景意图识别。如果不能确定,请输出 “未知意图”。<br>
Examples:
------------
text: 没关系啦 知道的
intent: 肯定答复
------------
text: 怎么能联系你
intent: 查联系方式
------------
text: 恩。让我想想吧。
intent: 考虑一下
------------
text: 说点有用的
intent: 请讲重点
------------
text: 唉唉
intent: 语气词
------------
text: 说快一点
intent: 请讲重点
------------
text: 再介绍一下
intent: 要求复述
------------
text: 从哪弄到我信息
intent: 质疑隐私安全
------------
text: 哎。。不是的
intent: 不是
------------
text: 给我电话号码
intent: 查联系方式
------------
text: 先看看吧
intent: 考虑一下
------------
text: 怎么知道道我的信息
intent: 质疑隐私安全
------------
text: 哎,再说吧,我再想想
intent: 考虑一下
------------
text: 不,我清醒。
intent: 不是
------------
text: 重说一次
intent: 要求复述
------------
text: 行了,晚安
intent: 肯定答复
------------
text: 额额额额
intent: 语气词
------------
text: 恩。哎再说吧我考虑一下hiahia
intent:
</code></pre>
</details>
<details>
<summary>train subset prompt 示例: (intent: 污言秽语)</summary>
<pre><code>电销场景意图识别。<br>
Examples:
text: 那留言
intent: 语音信箱<br>
text: 好啊,哈哈,没事,我再找其他的人
intent: 好的<br>
text: 在!
intent: 我在<br>
text: 要打副本,没时间
intent: 没时间<br>
text: 必须去学习!赶快去!
intent: 加快速度<br>
text: 好的。满汉全席送上
intent: 好的<br>
text: 你看到我给你的留言了么
intent: 语音信箱<br>
text: 我在呢。
intent: 我在<br>
text: 傻逼?
intent: 污言秽语<br>
text: 胸大无脑
intent: 污言秽语<br>
text: 不着急。
intent: 请等一等<br>
text: 恩 我是团子
intent: 做自我介绍<br>
text: 我是收电费的
intent: 做自我介绍<br>
text: 我现在没时间接电话呢,待会儿打给你。
intent: 没时间<br>
text: 好的。哈哈。初六见。我去睡觉啦
intent: 好的<br>
text: 在啊
intent: 我在<br>
text: 包皮猩
intent: 污言秽语<br>
text: 离开一下
intent: 请等一等<br>
text: 有病
intent: 污言秽语<br>
text: 给我留个言
intent: 语音信箱<br>
text: 你等一下
intent: 请等一等<br>
text: 立刻马上!!!快快快快
intent: 加快速度<br>
text: 我是郭钊源
intent: 做自我介绍<br>
text: 快点儿
intent: 加快速度<br>
text: 没时间睡觉怎么办吖
intent: 没时间<br>
text: 吃!你来
intent:
</code></pre>
</details>
<details>
<summary>test subset prompt 示例: (intent: 未能理解)</summary>
<pre><code>电销场景意图识别。如果不能确定,请输出 “未知意图”。<br>
Examples:
------------
text: 讲什么
intent: 未能理解
------------
text: 等着吧!
intent: 请等一等
------------
text: 搞不懂你
intent: 未能理解
------------
text: 我实在是不想弄了,我那时事多没时间啊!
intent: 没时间
------------
text: 这你自己不清楚自己啊,还不晓得
intent: 不清楚
------------
text: 没问题放心吧
intent: 肯定(没问题)
------------
text: 公司名字是什么
intent: 查公司介绍
------------
text: 不放弃
intent: 肯定(需要)
------------
text: 老师也不懂
intent:
</code></pre>
</details>
<details>
<summary>test subset prompt 示例: (intent: 肯定(嗯嗯))</summary>
<pre><code>电销场景意图识别。
不确定时请输出 “未知领域”。<br>
Examples:
------------
text: 截止期过了多少天
intent: 疑问(时长)
------------
text: 不了
intent: 不需要
------------
text: 不行,不够不够
intent: 否定(不可以)
------------
text: 4个1
intent: 答数值
------------
text: 辽宁
intent: 地址
------------
text: 不清楚
intent: 不清楚
------------
text: 店里
intent: 地址
------------
text: 嗯啊嗯嗯来吧
intent: 肯定(嗯嗯)
------------
text: 利息比别的贷款高
intent: 价格太高
------------
text: 算23点,[9,4,8,2
intent: 答数值
------------
text: 可以还得上
intent: 会按时处理
------------
text: 对啊 就是不行
intent: 否定(不可以)
------------
text: 真的不便宜
intent: 价格太高
------------
text: 嗯,thanks
intent: 肯定(嗯嗯)
------------
text: 这你自己不清楚自己啊,还不晓得
intent: 不清楚
------------
text: 我找找吧
intent: 会按时处理
------------
text: 这是拖欠几天了
intent: 疑问(时长)
------------
text: 不需要证据
intent: 不需要
------------
text: 噢,谢谢
intent: 肯定(嗯嗯)
------------
text: 恩恩,想我
intent:
</code></pre>
</details>
<details>
<summary>test subset prompt 示例: (intent: 不信任)</summary>
<pre><code>意图识别。<br>
Examples:
text: 你不要答非所问
intent: 答非所问<br>
text: 费用搞错了
intent: 否定(错误)<br>
text: 我给你留言了,你木有回
intent: 语音信箱<br>
text: 小骗子
intent: 不信任<br>
text: 昆明
intent: 实体(地址)<br>
text: 哦,行,好了你发信息给我
intent: 肯定(可以)<br>
text: 哦,这样啊,没时间就算了
intent: 没时间<br>
text: 我错了,别欺负我了
intent: 请求谅解<br>
text: 万一你们是骗子怎么办
intent: 不信任<br>
text: 我太乃刀了
intent: 无关领域<br>
text: 讲清楚重要的
intent: 请讲重点<br>
text: 骗子,好好说话
intent:
</code></pre>
</details>
### 数据来源
数据集从网上收集整理如下:
#### 意图识别
意图识别(英语)
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| ATIS | 英语 | [ATIS](https://paperswithcode.com/dataset/atis); [ATIS_dataset](https://github.com/howl-anderson/ATIS_dataset) | 4978(Training set)+893(Testing set) | 微软提供的公开数据集 (Airline Travel Information System),实现意图识别任务。 | [atis_intents](https://huggingface.co/datasets/fathyshalab/atis_intents) |
| conv_intent | 英语 | [conv_intent](https://huggingface.co/datasets/generalization/conv_intent_Full-p_1) | 13.8K | | [intent-recogniton](https://www.kaggle.com/code/upsunny/intent-recogniton-based-on-bert) |
| banking77 | 英语 | [banking77](https://arxiv.org/abs/2003.04807); [task-specific-datasets](https://github.com/PolyAI-LDN/task-specific-datasets) | 13,083 | 在线银行查询数据集 | [banking77](https://huggingface.co/datasets/banking77) |
| mobile_assistant | 英语 | [Intent-Classification-large](https://huggingface.co/datasets/dipesh/Intent-Classification-large) | 17K (但是我去除了意图为 others 的样本.) | | |
| amazon_massive_intent_en_us | 英语 | [amazon_massive_intent_en_us](https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US) | 16.5K | Alexa virtual assistant | [nlu_evaluation_data](https://huggingface.co/datasets/nlu_evaluation_data) |
| snips_built_in_intents | 英语 | [nlu-benchmark](https://github.com/sonos/nlu-benchmark); [benchmarking](https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d) | 328 | | [snips_built_in_intents](https://huggingface.co/datasets/snips_built_in_intents) |
| vira_intents | 英语 | [vira-intent-classification](https://github.com/IBM/vira-intent-classification) | 10.9K | COVID-19 疫苗意图 | [vira_intents_live](https://huggingface.co/datasets/codesj/vira-intents-live); [vira_intents_live](https://huggingface.co/datasets/vira-chatbot/vira-intents-live) |
| intent_classification | 英语 | [intent_classification](https://huggingface.co/datasets/Bhuvaneshwari/intent_classification) | 13.8K | | |
| Out-of-Scope | 英语 | [范围外意图分类数据集](https://tianchi.aliyun.com/dataset/94112); [clinc150](https://archive.ics.uci.edu/dataset/570/clinc150) | | 该数据集提供了一种评估“Out-of-Scope”输入的意图分类模型的方法。 | [Out-of-Scope Intent Classification Dataset](https://www.kaggle.com/datasets/stefanlarson/outofscope-intent-classification-dataset); [clinc_oos](https://huggingface.co/datasets/clinc_oos) |
| finance21 | 英语 | [finance21](https://github.com/Dark-Sied/Intent_Classification/) | | | |
| book6 | 英语 | [book6](https://github.com/ajinkyaT/CNN_Intent_Classification) | 12000 | Six categories namely: AddToPlaylist, BookRestaurant, GetWeather , RateBook , SearchCreativeWork, SearchScreeningEvent each having nearly 2000 sentences. | [Intent Recognition Dataset](https://www.kaggle.com/datasets/himanshunayal/intent-recognition-dataset) |
| bi_text | 英语 | [bi_text](https://www.kaggle.com/datasets/bitext/training-dataset-for-chatbotsvirtual-assistants); [customer-support-intent-dataset](https://www.kaggle.com/datasets/scodepy/customer-support-intent-dataset) | 8175 | 该数据集涵盖“客户支持”领域,包括分为 11 个类别的 27 个意图。 这些意图是从 Bitext 的 20 个特定领域数据集(银行、零售、公用事业……)中选择的,保留了跨领域的通用意图。 | |
| small talk | 英语 | [Small Talk](https://www.kaggle.com/datasets/salmanfaroz/small-talk-intent-classification-data) | 3000 | 闲聊用于为用户提供与聊天机器人的随意对话流程 | |
| chatbots | 英语 | [Chatbots: Intent Recognition Dataset](https://www.kaggle.com/datasets/elvinagammed/chatbots-intent-recognition-dataset) | | 用于分类、识别和聊天机器人开发的数据 | |
| ide_intent | 英语 | [intent-classification-for-ide-functionalities](https://www.kaggle.com/datasets/abdullahusmani86/intent-classification-for-ide-functionalities) | 27019 | IDE 意图分类数据集。 | |
| star_wars | 英语 | [star-wars](https://www.kaggle.com/datasets/aslanahmedov/star-wars-chat-bot) | 100 | 包含有关星球大战宇宙的各种数据。 | |
| jarvis_intent | 英语 | [jarvisintent](https://www.kaggle.com/datasets/joelyu/jarvisintent) | 4556 | | |
意图识别(汉语)
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| amazon_massive_intent_zh_cn | 汉语 | [amazon_massive_intent_zh_cn](https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN) | 16.5K | Alexa virtual assistant | |
| THU Intent Corpus | 汉语 | | 共计约6,000个句子 | 清华大学发布的中文意图识别和词槽填充数据集,包含15个领域和27个意图类别 | |
| CrossWOZ | 汉语 | [CrossWOZ](https://github.com/thu-coai/CrossWOZ) | | CrossWOZ是第一个大规模中文跨域Wizard-of-Oz任务导向数据集。 它包含 5 个领域的 6K 对话会话和 102K 话语,包括酒店、餐厅、景点、地铁和出租车。 此外,该语料库还包含用户侧和系统侧丰富的对话状态和对话行为注释。 | |
| CMID | 汉语 | [CMID](https://github.com/ishine/CMID) | | 该数据集用于中文医学 QA 意图理解任务。 | |
| dmslots | 汉语 | [dmslots](https://raw.githubusercontent.com/kids/bert_nlu/main/data/dmslots.txt) | | 弱标注数据 | |
| SMP2017 | 汉语 | [SMP2017-ECDT](http://ir.hit.edu.cn/SMP2017-ECDT); [1709.10217](https://arxiv.org/abs/1709.10217); [SMP2017ECDT-DATA](https://github.com/HITlilingzhi/SMP2017ECDT-DATA) | | 第六届全国社会媒体处理大会之中文人机对话技术评测(SMP2017-ECDT) | [ChineseNLPCorpus](https://github.com/InsaneLife/ChineseNLPCorpus) |
| SMP2019 | 汉语 | [SMP2019](https://conference.cipsc.org.cn/smp2019/evaluation.html); [smp2019ecdt_task1](https://adamszq.github.io/smp2019ecdt_task1/) | | SMP2019 ECDT 中文人机对话技术测评 | [SMP2017-2019-ECDT-data](https://github.com/hml-ubt/SMP2017-2019-ECDT-data); [ChineseNLPCorpus](https://github.com/InsaneLife/ChineseNLPCorpus) |
| a_intent | 汉语 | [意图识别](https://blog.csdn.net/weixin_42551154/article/details/129480825); [a_intent](https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi) | 12000 | 该意图识别数据集是一个多分类任务,目标是根据用户的输入文本判断用户的意图 | |
| RiSAWOZ | 汉语 | [RiSAWOZ](https://gem-benchmark.com/data_cards/RiSAWOZ) | | RiSAWOZ 是一个中文对话数据集。 它可用于研究各种对话任务,例如对话状态跟踪、对话上下文到文本生成、共指消解以及统一生成省略号和共指消解。 | [GEM/RiSAWOZ](https://huggingface.co/datasets/GEM/RiSAWOZ) |
| IMCS-IR | 汉语 | [中文医疗信息处理评测基准CBLUE](https://tianchi.aliyun.com/dataset/95414); [CBLUE 智能对话诊疗意图识别 IMCS-IR](https://github.com/winninghealth/imcs-ir) | | 中文医疗信息处理挑战榜CBLUE | |
#### 文本分类
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| ag_news | 英语 | [AG_corpus_of_news_articles](http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html); [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626); [ag_news](https://huggingface.co/datasets/ag_news) | 120K | AG的新闻主题分类数据集 | |
| daily_dialog | 英语 | [DailyDialog](http://yanran.li/dailydialog) | 11.1K | 标签分类为:dummy (0), inform (1), question (2), directive (3), commissive (4). 情感分类为:no emotion (0), anger (1), disgust (2), fear (3), happiness (4), sadness (5), surprise (6). | [daily_dialog](https://huggingface.co/datasets/daily_dialog) |
| chinese_news_title | 汉语 | [中文新闻文本标题分类](https://aistudio.baidu.com/datasetdetail/103654) | | 中文新闻标题数据集包含可供训练的32类(即新闻主题)标题47,952个,可供测试的新闻标题15,986个。在删除这些包含不能处理的特殊字符的标题后,我们保留了47,850个训练标题和15,950个测试标题(即#DataSet1)。 | [百度网盘](https://pan.baidu.com/s/1mgBTFOO) |
#### 其它任务类型
| 数据 | 语言 | 任务类型 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :-----: | :---: | :---: | :---: | :---: |
| suicide_intent | 英语 | 情感分类 | [suicide-intent](https://www.kaggle.com/datasets/hetarthraval/suicide-intent-detection-dataset) | 3731 | 该数据集有四个类别:快乐、正常、悲伤和自杀意图。 | |
| CARER | 英语 | 情感分类 | [emotion](https://paperswithcode.com/dataset/emotion) | 20K | 情感是英语 Twitter 消息的数据集,包含六种基本情感:愤怒、恐惧、快乐、爱、悲伤和惊讶。 | [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) |
| COIG-CQIA | 汉语 | 指令微调 | [CValues](https://arxiv.org/abs/2307.09705); [paralym/COIG-CQIA](https://github.com/paralym/COIG-CQIA) | | 高质量指令微调数据集,旨在为中文NLP社区提供高质量且符合人类交互行为的指令微调数据。 | [m-a-p/COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA) |
| emo2019 | 英语 | 情感分类 | [SemEval-2019 Task 3](https://www.aclweb.org/anthology/S19-2005) | TRAIN: 30160, TEST: 5509 | 情绪检测。四个标签:others (0), happy (1), sad (2), angry (3). | [emo](https://huggingface.co/datasets/emo) |
### 数据加载
```python
#!/usr/bin/python3
# -*- coding: utf-8 -*-
from datasets import load_dataset, concatenate_datasets
name_list = [
"amazon_massive_intent_en_us_prompt",
"amazon_massive_intent_zh_cn_prompt",
"atis_intent_prompt",
"banking77_prompt",
"bi_text11_prompt",
"bi_text27_prompt",
"book6_prompt",
# "chinese_news_title_prompt",
"cmid_4class_prompt",
"cmid_36class_prompt",
"conv_intent_prompt",
"crosswoz_prompt",
"dmslots_prompt",
"finance21_prompt",
"intent_classification_prompt",
"mobile_assistant_prompt",
"mtop_intent_prompt",
"out_of_scope_prompt",
"small_talk_prompt",
"smp2017_task1_prompt",
"smp2019_task1_domain_prompt",
"smp2019_task1_intent_prompt",
"snips_built_in_intents_prompt",
"telemarketing_intent_en_prompt",
"telemarketing_intent_cn_prompt",
"vira_intents_prompt",
]
train_dataset = list()
for name in name_list:
dataset = load_dataset(
path="qgyd2021/few_shot_intent_sft",
name=name,
split="train",
)
train_dataset.append(dataset)
train_dataset = concatenate_datasets(train_dataset)
valid_dataset = list()
for name in name_list:
dataset = load_dataset(
path="qgyd2021/few_shot_intent_sft",
name=name,
split="test",
)
valid_dataset.append(dataset)
valid_dataset = concatenate_datasets(valid_dataset)
```
### 参考来源
<details>
<summary>参考的数据来源,展开查看</summary>
<pre><code>
https://huggingface.co/datasets/qanastek/MASSIVE
https://huggingface.co/datasets/fathyshalab/atis_intents
https://huggingface.co/datasets/generalization/conv_intent_Full-p_1
https://huggingface.co/datasets/banking77
https://huggingface.co/datasets/dipesh/Intent-Classification-large
https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US
https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN
https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-TW
https://huggingface.co/datasets/snips_built_in_intents
https://huggingface.co/datasets/zapsdcn/citation_intent
https://huggingface.co/datasets/ibm/vira-intents
https://huggingface.co/datasets/mteb/mtop_intent
https://huggingface.co/datasets/Bhuvaneshwari/intent_classification
https://huggingface.co/datasets/ibm/vira-intents-live
https://huggingface.co/datasets/ebrigham/nl_banking_intents
https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi
https://gitee.com/a2798063/SMP2019/tree/master
https://cold-eye.github.io/post/nlp-corpus/
https://www.cluebenchmarks.com/introduce.html
https://github.com/search?q=chinese%20intent&type=repositories
</code></pre>
</details>
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laion/dalle-3-dataset | laion | 2023-12-18T00:44:58Z | 1,610 | 218 | [
"language:en",
"license:cc0-1.0",
"image-text-dataset",
"synthetic-dataset",
"region:us"
] | null | 2023-10-06T18:11:38Z | ---
language:
- en
license:
- cc0-1.0
tags:
- image-text-dataset
- synthetic-dataset
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for LAION DALL·E 3 Discord Dataset
**Description**: This dataset consists of prompt and image URL pairs scraped from the LAION [share-dalle-3 discord channel](https://discord.com/channels/823813159592001537/1158354590463447092). The purpose is to collect image-text pairs for research and exploration.
**Source Code**: The code used to generate this data can be found [here](https://github.com/LAION-AI/Discord-Scrapers.git).
## Contributors
- [Zach Nagengast](https://github.com/ZachNagengast)
- [Eduardo Pach](https://github.com/EduardoPach)
- [Seva Maltsev](https://github.com/TwoAbove)
- The [LAION community](https://discord.com/invite/eq3cAMZtCC)
## Data Attributes
- **caption**: The text description or prompt associated with the image. Data type: string.
- **image**: The embedded image data from the discord message attachment. Data type: image.
- **link**: The URL to the associated image. Data type: string.
- **message_id**: The discord message id where the image was posted. Data type: string.
- **timestamp**: Time the original message was posted. Datatype: string. | [
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203427as321/articles | 203427as321 | 2023-12-18T01:00:07Z | 1,479 | 0 | [
"region:us"
] | null | 2023-05-25T19:13:43Z | ---
dataset_info:
features:
- name: label
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: float64
splits:
- name: train
num_bytes: 23996247
num_examples: 1534
download_size: 0
dataset_size: 23996247
---
# Dataset Card for "articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
HAERAE-HUB/K-MMLU-Preview | HAERAE-HUB | 2023-12-16T10:38:26Z | 1,329 | 4 | [
"task_categories:multiple-choice",
"size_categories:10K<n<100K",
"language:ko",
"license:cc-by-nc-nd-4.0",
"mmlu",
"haerae",
"region:us"
] | [
"multiple-choice"
] | 2023-11-27T09:06:18Z | ---
configs:
- config_name: easy-Agricultural-Sciences
data_files:
- split: train
path: data/[easy]-Agricultural-Sciences-train.csv
- split: dev
path: data/[easy]-Agricultural-Sciences-dev.csv
- split: test
path: data/[easy]-Agricultural-Sciences-test.csv
- config_name: easy-Aviation-Engineering-and-Maintenance
data_files:
- split: train
path: data/[easy]-Aviation-Engineering-and-Maintenance-train.csv
- split: dev
path: data/[easy]-Aviation-Engineering-and-Maintenance-dev.csv
- split: test
path: data/[easy]-Aviation-Engineering-and-Maintenance-test.csv
- config_name: easy-Biology
data_files:
- split: train
path: data/[easy]-Biology-train.csv
- split: dev
path: data/[easy]-Biology-dev.csv
- split: test
path: data/[easy]-Biology-test.csv
- config_name: easy-Chemical-Engineering
data_files:
- split: train
path: data/[easy]-Chemical-Engineering-train.csv
- split: dev
path: data/[easy]-Chemical-Engineering-dev.csv
- split: test
path: data/[easy]-Chemical-Engineering-test.csv
- config_name: easy-Chemistry
data_files:
- split: train
path: data/[easy]-Chemistry-train.csv
- split: dev
path: data/[easy]-Chemistry-dev.csv
- split: test
path: data/[easy]-Chemistry-test.csv
- config_name: easy-Civil-Engineering
data_files:
- split: train
path: data/[easy]-Civil-Engineering-train.csv
- split: dev
path: data/[easy]-Civil-Engineering-dev.csv
- split: test
path: data/[easy]-Civil-Engineering-test.csv
- config_name: easy-Computer-Science
data_files:
- split: train
path: data/[easy]-Computer-Science-train.csv
- split: dev
path: data/[easy]-Computer-Science-dev.csv
- split: test
path: data/[easy]-Computer-Science-test.csv
- config_name: easy-Construction
data_files:
- split: train
path: data/[easy]-Construction-train.csv
- split: dev
path: data/[easy]-Construction-dev.csv
- split: test
path: data/[easy]-Construction-test.csv
- config_name: easy-Ecology
data_files:
- split: train
path: data/[easy]-Ecology-train.csv
- split: dev
path: data/[easy]-Ecology-dev.csv
- split: test
path: data/[easy]-Ecology-test.csv
- config_name: easy-Electrical-Engineering
data_files:
- split: train
path: data/[easy]-Electrical-Engineering-train.csv
- split: dev
path: data/[easy]-Electrical-Engineering-dev.csv
- split: test
path: data/[easy]-Electrical-Engineering-test.csv
- config_name: easy-Electronics-Engineering
data_files:
- split: train
path: data/[easy]-Electronics-Engineering-train.csv
- split: dev
path: data/[easy]-Electronics-Engineering-dev.csv
- split: test
path: data/[easy]-Electronics-Engineering-test.csv
- config_name: easy-Energy-Management
data_files:
- split: train
path: data/[easy]-Energy-Management-train.csv
- split: dev
path: data/[easy]-Energy-Management-dev.csv
- split: test
path: data/[easy]-Energy-Management-test.csv
- config_name: easy-Environmental-Science
data_files:
- split: train
path: data/[easy]-Environmental-Science-train.csv
- split: dev
path: data/[easy]-Environmental-Science-dev.csv
- split: test
path: data/[easy]-Environmental-Science-test.csv
- config_name: easy-Fashion
data_files:
- split: train
path: data/[easy]-Fashion-train.csv
- split: dev
path: data/[easy]-Fashion-dev.csv
- split: test
path: data/[easy]-Fashion-test.csv
- config_name: easy-Food-Processing
data_files:
- split: train
path: data/[easy]-Food-Processing-train.csv
- split: dev
path: data/[easy]-Food-Processing-dev.csv
- split: test
path: data/[easy]-Food-Processing-test.csv
- config_name: easy-Gas-Technology-and-Engineering
data_files:
- split: train
path: data/[easy]-Gas-Technology-and-Engineering-train.csv
- split: dev
path: data/[easy]-Gas-Technology-and-Engineering-dev.csv
- split: test
path: data/[easy]-Gas-Technology-and-Engineering-test.csv
- config_name: easy-Geomatics
data_files:
- split: train
path: data/[easy]-Geomatics-train.csv
- split: dev
path: data/[easy]-Geomatics-dev.csv
- split: test
path: data/[easy]-Geomatics-test.csv
- config_name: easy-Industrial-Engineer
data_files:
- split: train
path: data/[easy]-Industrial-Engineer-train.csv
- split: dev
path: data/[easy]-Industrial-Engineer-dev.csv
- split: test
path: data/[easy]-Industrial-Engineer-test.csv
- config_name: easy-Information-Technology
data_files:
- split: train
path: data/[easy]-Information-Technology-train.csv
- split: dev
path: data/[easy]-Information-Technology-dev.csv
- split: test
path: data/[easy]-Information-Technology-test.csv
- config_name: easy-Interior-Architecture-and-Design
data_files:
- split: train
path: data/[easy]-Interior-Architecture-and-Design-train.csv
- split: dev
path: data/[easy]-Interior-Architecture-and-Design-dev.csv
- split: test
path: data/[easy]-Interior-Architecture-and-Design-test.csv
- config_name: easy-Law
data_files:
- split: train
path: data/[easy]-Law-train.csv
- split: dev
path: data/[easy]-Law-dev.csv
- split: test
path: data/[easy]-Law-test.csv
- config_name: easy-Machine-Design-and-Manufacturing
data_files:
- split: train
path: data/[easy]-Machine-Design-and-Manufacturing-train.csv
- split: dev
path: data/[easy]-Machine-Design-and-Manufacturing-dev.csv
- split: test
path: data/[easy]-Machine-Design-and-Manufacturing-test.csv
- config_name: easy-Management
data_files:
- split: train
path: data/[easy]-Management-train.csv
- split: dev
path: data/[easy]-Management-dev.csv
- split: test
path: data/[easy]-Management-test.csv
- config_name: easy-Maritime-Engineering
data_files:
- split: train
path: data/[easy]-Maritime-Engineering-train.csv
- split: dev
path: data/[easy]-Maritime-Engineering-dev.csv
- split: test
path: data/[easy]-Maritime-Engineering-test.csv
- config_name: easy-Marketing
data_files:
- split: train
path: data/[easy]-Marketing-train.csv
- split: dev
path: data/[easy]-Marketing-dev.csv
- split: test
path: data/[easy]-Marketing-test.csv
- config_name: easy-Materials-Engineering
data_files:
- split: train
path: data/[easy]-Materials-Engineering-train.csv
- split: dev
path: data/[easy]-Materials-Engineering-dev.csv
- split: test
path: data/[easy]-Materials-Engineering-test.csv
- config_name: easy-Mechanical-Engineering
data_files:
- split: train
path: data/[easy]-Mechanical-Engineering-train.csv
- split: dev
path: data/[easy]-Mechanical-Engineering-dev.csv
- split: test
path: data/[easy]-Mechanical-Engineering-test.csv
- config_name: easy-Nondestructive-Testing
data_files:
- split: train
path: data/[easy]-Nondestructive-Testing-train.csv
- split: dev
path: data/[easy]-Nondestructive-Testing-dev.csv
- split: test
path: data/[easy]-Nondestructive-Testing-test.csv
- config_name: easy-Patent
data_files:
- split: train
path: data/[easy]-Patent-train.csv
- split: dev
path: data/[easy]-Patent-dev.csv
- split: test
path: data/[easy]-Patent-test.csv
- config_name: easy-Psychology
data_files:
- split: train
path: data/[easy]-Psychology-train.csv
- split: dev
path: data/[easy]-Psychology-dev.csv
- split: test
path: data/[easy]-Psychology-test.csv
- config_name: easy-Public-Safety
data_files:
- split: train
path: data/[easy]-Public-Safety-train.csv
- split: dev
path: data/[easy]-Public-Safety-dev.csv
- split: test
path: data/[easy]-Public-Safety-test.csv
- config_name: easy-Railway-and-Automotive-Engineering
data_files:
- split: train
path: data/[easy]-Railway-and-Automotive-Engineering-train.csv
- split: dev
path: data/[easy]-Railway-and-Automotive-Engineering-dev.csv
- split: test
path: data/[easy]-Railway-and-Automotive-Engineering-test.csv
- config_name: easy-Refrigerating-Machinery
data_files:
- split: train
path: data/[easy]-Refrigerating-Machinery-train.csv
- split: dev
path: data/[easy]-Refrigerating-Machinery-dev.csv
- split: test
path: data/[easy]-Refrigerating-Machinery-test.csv
- config_name: easy-Social-Welfare
data_files:
- split: train
path: data/[easy]-Social-Welfare-train.csv
- split: dev
path: data/[easy]-Social-Welfare-dev.csv
- split: test
path: data/[easy]-Social-Welfare-test.csv
- config_name: easy-Telecommunications-and-Wireless-Technology
data_files:
- split: train
path: data/[easy]-Telecommunications-and-Wireless-Technology-train.csv
- split: dev
path: data/[easy]-Telecommunications-and-Wireless-Technology-dev.csv
- split: test
path: data/[easy]-Telecommunications-and-Wireless-Technology-test.csv
- config_name: hard-Accounting
data_files:
- split: train
path: data/[hard]-Accounting-train.csv
- split: dev
path: data/[hard]-Accounting-dev.csv
- split: test
path: data/[hard]-Accounting-test.csv
- config_name: hard-Agricultural-Sciences
data_files:
- split: train
path: data/[hard]-Agricultural-Sciences-train.csv
- split: dev
path: data/[hard]-Agricultural-Sciences-dev.csv
- split: test
path: data/[hard]-Agricultural-Sciences-test.csv
- config_name: hard-Biology
data_files:
- split: train
path: data/[hard]-Biology-train.csv
- split: dev
path: data/[hard]-Biology-dev.csv
- split: test
path: data/[hard]-Biology-test.csv
- config_name: hard-Chemical-Engineering
data_files:
- split: train
path: data/[hard]-Chemical-Engineering-train.csv
- split: dev
path: data/[hard]-Chemical-Engineering-dev.csv
- split: test
path: data/[hard]-Chemical-Engineering-test.csv
- config_name: hard-Chemistry
data_files:
- split: train
path: data/[hard]-Chemistry-train.csv
- split: dev
path: data/[hard]-Chemistry-dev.csv
- split: test
path: data/[hard]-Chemistry-test.csv
- config_name: hard-Civil-Engineering
data_files:
- split: train
path: data/[hard]-Civil-Engineering-train.csv
- split: dev
path: data/[hard]-Civil-Engineering-dev.csv
- split: test
path: data/[hard]-Civil-Engineering-test.csv
- config_name: hard-Computer-Science
data_files:
- split: train
path: data/[hard]-Computer-Science-train.csv
- split: dev
path: data/[hard]-Computer-Science-dev.csv
- split: test
path: data/[hard]-Computer-Science-test.csv
- config_name: hard-Construction
data_files:
- split: train
path: data/[hard]-Construction-train.csv
- split: dev
path: data/[hard]-Construction-dev.csv
- split: test
path: data/[hard]-Construction-test.csv
- config_name: hard-Criminal-Law
data_files:
- split: train
path: data/[hard]-Criminal-Law-train.csv
- split: dev
path: data/[hard]-Criminal-Law-dev.csv
- split: test
path: data/[hard]-Criminal-Law-test.csv
- config_name: hard-Economics
data_files:
- split: train
path: data/[hard]-Economics-train.csv
- split: dev
path: data/[hard]-Economics-dev.csv
- split: test
path: data/[hard]-Economics-test.csv
- config_name: hard-Education
data_files:
- split: train
path: data/[hard]-Education-train.csv
- split: dev
path: data/[hard]-Education-dev.csv
- split: test
path: data/[hard]-Education-test.csv
- config_name: hard-Electrical-Engineering
data_files:
- split: train
path: data/[hard]-Electrical-Engineering-train.csv
- split: dev
path: data/[hard]-Electrical-Engineering-dev.csv
- split: test
path: data/[hard]-Electrical-Engineering-test.csv
- config_name: hard-Electronics-Engineering
data_files:
- split: train
path: data/[hard]-Electronics-Engineering-train.csv
- split: dev
path: data/[hard]-Electronics-Engineering-dev.csv
- split: test
path: data/[hard]-Electronics-Engineering-test.csv
- config_name: hard-Energy-Management
data_files:
- split: train
path: data/[hard]-Energy-Management-train.csv
- split: dev
path: data/[hard]-Energy-Management-dev.csv
- split: test
path: data/[hard]-Energy-Management-test.csv
- config_name: hard-Food-Processing
data_files:
- split: train
path: data/[hard]-Food-Processing-train.csv
- split: dev
path: data/[hard]-Food-Processing-dev.csv
- split: test
path: data/[hard]-Food-Processing-test.csv
- config_name: hard-Gas-Technology-and-Engineering
data_files:
- split: train
path: data/[hard]-Gas-Technology-and-Engineering-train.csv
- split: dev
path: data/[hard]-Gas-Technology-and-Engineering-dev.csv
- split: test
path: data/[hard]-Gas-Technology-and-Engineering-test.csv
- config_name: hard-Geomatics
data_files:
- split: train
path: data/[hard]-Geomatics-train.csv
- split: dev
path: data/[hard]-Geomatics-dev.csv
- split: test
path: data/[hard]-Geomatics-test.csv
- config_name: hard-Health
data_files:
- split: train
path: data/[hard]-Health-train.csv
- split: dev
path: data/[hard]-Health-dev.csv
- split: test
path: data/[hard]-Health-test.csv
- config_name: hard-Industrial-Engineer
data_files:
- split: train
path: data/[hard]-Industrial-Engineer-train.csv
- split: dev
path: data/[hard]-Industrial-Engineer-dev.csv
- split: test
path: data/[hard]-Industrial-Engineer-test.csv
- config_name: hard-Information-Technology
data_files:
- split: train
path: data/[hard]-Information-Technology-train.csv
- split: dev
path: data/[hard]-Information-Technology-dev.csv
- split: test
path: data/[hard]-Information-Technology-test.csv
- config_name: hard-Law
data_files:
- split: train
path: data/[hard]-Law-train.csv
- split: dev
path: data/[hard]-Law-dev.csv
- split: test
path: data/[hard]-Law-test.csv
- config_name: hard-Machine-Design-and-Manufacturing
data_files:
- split: train
path: data/[hard]-Machine-Design-and-Manufacturing-train.csv
- split: dev
path: data/[hard]-Machine-Design-and-Manufacturing-dev.csv
- split: test
path: data/[hard]-Machine-Design-and-Manufacturing-test.csv
- config_name: hard-Management
data_files:
- split: train
path: data/[hard]-Management-train.csv
- split: dev
path: data/[hard]-Management-dev.csv
- split: test
path: data/[hard]-Management-test.csv
- config_name: hard-Materials-Engineering
data_files:
- split: train
path: data/[hard]-Materials-Engineering-train.csv
- split: dev
path: data/[hard]-Materials-Engineering-dev.csv
- split: test
path: data/[hard]-Materials-Engineering-test.csv
- config_name: hard-Political-Science-and-Sociology
data_files:
- split: train
path: data/[hard]-Political-Science-and-Sociology-train.csv
- split: dev
path: data/[hard]-Political-Science-and-Sociology-dev.csv
- split: test
path: data/[hard]-Political-Science-and-Sociology-test.csv
- config_name: hard-Psychology
data_files:
- split: train
path: data/[hard]-Psychology-train.csv
- split: dev
path: data/[hard]-Psychology-dev.csv
- split: test
path: data/[hard]-Psychology-test.csv
- config_name: hard-Public-Safety
data_files:
- split: train
path: data/[hard]-Public-Safety-train.csv
- split: dev
path: data/[hard]-Public-Safety-dev.csv
- split: test
path: data/[hard]-Public-Safety-test.csv
- config_name: hard-Railway-and-Automotive-Engineering
data_files:
- split: train
path: data/[hard]-Railway-and-Automotive-Engineering-train.csv
- split: dev
path: data/[hard]-Railway-and-Automotive-Engineering-dev.csv
- split: test
path: data/[hard]-Railway-and-Automotive-Engineering-test.csv
- config_name: hard-Real-Estate
data_files:
- split: train
path: data/[hard]-Real-Estate-train.csv
- split: dev
path: data/[hard]-Real-Estate-dev.csv
- split: test
path: data/[hard]-Real-Estate-test.csv
- config_name: hard-Social-Welfare
data_files:
- split: train
path: data/[hard]-Social-Welfare-train.csv
- split: dev
path: data/[hard]-Social-Welfare-dev.csv
- split: test
path: data/[hard]-Social-Welfare-test.csv
- config_name: hard-Taxation
data_files:
- split: train
path: data/[hard]-Taxation-train.csv
- split: dev
path: data/[hard]-Taxation-dev.csv
- split: test
path: data/[hard]-Taxation-test.csv
- config_name: hard-Telecommunications-and-Wireless-Technology
data_files:
- split: train
path: data/[hard]-Telecommunications-and-Wireless-Technology-train.csv
- split: dev
path: data/[hard]-Telecommunications-and-Wireless-Technology-dev.csv
- split: test
path: data/[hard]-Telecommunications-and-Wireless-Technology-test.csv
license: cc-by-nc-nd-4.0
task_categories:
- multiple-choice
language:
- ko
tags:
- mmlu
- haerae
size_categories:
- 10K<n<100K
---
# K-MMLU (Korean-MMLU)
<font color='red'>🚧 We're updating the dataset.🚧</font>
*Paper Coming Soon!*
The K-MMLU (Korean-MMLU) is a comprehensive suite designed to evaluate the advanced knowledge and reasoning abilities of large language models (LLMs)
within the Korean language and cultural context. This suite encompasses 45 topics, primarily focusing on expert-level subjects.
It includes general subjects like Physics and Ecology, and law and political science, alongside specialized fields such as Non-Destructive Training and Maritime Engineering.
The datasets are derived from Korean licensing exams, with about 90% of the questions including human accuracy based on the performance of human test-takers in these exams.
K-MMLU is segmented into training, testing, and development subsets, with the test subset ranging from a minimum of 100 to a maximum of 1000 questions, totaling 35,000 questions.
Additionally, a set of 10 questions is provided as a development set for few-shot exemplar development. At total, K-MMLU consists of 254,334 instances.
### Usage via LM-Eval-Harness
Official implementation for the evaluation is now available! You may run the evaluations yourself by:
```python
lm_eval --model hf \
--model_args pretrained=NousResearch/Llama-2-7b-chat-hf,dtype=float16 \
--num_fewshot 0 \
--batch_size 4 \
--tasks kmmlu \
--device cuda:0
```
To install lm-eval-harness refer to : [https://github.com/EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
### Point of Contact
For any questions contact us via the following email:)
```
[email protected]
``` | [
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jherng/xd-violence | jherng | 2023-12-17T10:40:51Z | 1,177 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-19T10:52:59Z | ---
license: mit
dataset_info:
config_name: video
features:
- name: id
dtype: string
- name: path
dtype: string
- name: binary_target
dtype:
class_label:
names:
'0': Non-violence
'1': Violence
- name: multilabel_targets
sequence:
class_label:
names:
'0': Normal
'1': Fighting
'2': Shooting
'3': Riot
'4': Abuse
'5': Car accident
'6': Explosion
- name: frame_annotations
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
splits:
- name: train
num_bytes: 780973
num_examples: 3942
- name: test
num_bytes: 118898
num_examples: 500
download_size: 79370542052
dataset_size: 899871
---
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argilla/end2end_textclassification | argilla | 2023-12-15T16:51:44Z | 1,074 | 0 | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-11-13T17:25:52Z | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for end2end_textclassification
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/end2end_textclassification")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/end2end_textclassification")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | Label | label_selection | True | N/A | ['World', 'Sports', 'Business', 'Sci/Tech'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": "record-0",
"fields": {
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
},
"metadata": {},
"responses": [],
"suggestions": [],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": "record-0",
"label": [],
"label-suggestion": null,
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"metadata": "{}",
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
Classify the articles into one of the four categories.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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ai4privacy/pii-masking-200k | ai4privacy | 2023-12-12T11:23:18Z | 981 | 16 | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:fr",
"language:de",
"language:it",
"legal",
"business",
"psychology",
"privacy",
"region:us"
] | [
"conversational",
"text-classification",
"token-classification",
"table-question-answering",
"question-answering",
"zero-shot-classification",
"summarization",
"feature-extraction",
"text-generation",
"text2text-generation"
] | 2023-11-06T03:34:07Z | ---
language:
- en
- fr
- de
- it
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
multilinguality:
- multilingual
tags:
- legal
- business
- psychology
- privacy
size_categories:
- 100K<n<1M
pretty_name: Ai4Privacy PII200k Dataset
source_datasets:
- original
---
# Purpose and Features
World's largest open source privacy dataset.
The purpose of the dataset is to train models to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
The example texts have **54 PII classes** (types of sensitive data), targeting **229 discussion subjects / use cases** split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).
Key facts:
- Size: 13.6m text tokens in ~209k examples with 649k PII tokens (see [summary.json](summary.json))
- 4 languages, more to come!
- English
- French
- German
- Italian
- Synthetic data generated using proprietary algorithms
- No privacy violations!
- Human-in-the-loop validated high quality dataset
# Getting started
Option 1: Python
```terminal
pip install datasets
```
```python
from datasets import load_dataset
dataset = load_dataset("ai4privacy/pii-masking-200k", data_files=["*.jsonl"])
```
# Token distribution across PII classes
We have taken steps to balance the token distribution across PII classes covered by the dataset.
This graph shows the distribution of observations across the different PII classes in this release:

There is 1 class that is still overrepresented in the dataset: firstname.
We will further improve the balance with future dataset releases.
This is the token distribution excluding the FIRSTNAME class:

# Compatible Machine Learning Tasks:
- Tokenclassification. Check out a HuggingFace's [guide on token classification](https://huggingface.co/docs/transformers/tasks/token_classification).
- [ALBERT](https://huggingface.co/docs/transformers/model_doc/albert), [BERT](https://huggingface.co/docs/transformers/model_doc/bert), [BigBird](https://huggingface.co/docs/transformers/model_doc/big_bird), [BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt), [BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom), [BROS](https://huggingface.co/docs/transformers/model_doc/bros), [CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert), [CANINE](https://huggingface.co/docs/transformers/model_doc/canine), [ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert), [Data2VecText](https://huggingface.co/docs/transformers/model_doc/data2vec-text), [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta), [DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2), [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert), [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra), [ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie), [ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m), [ESM](https://huggingface.co/docs/transformers/model_doc/esm), [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon), [FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert), [FNet](https://huggingface.co/docs/transformers/model_doc/fnet), [Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel), [GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo), [GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox), [I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert), [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm), [LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3), [LiLT](https://huggingface.co/docs/transformers/model_doc/lilt), [Longformer](https://huggingface.co/docs/transformers/model_doc/longformer), [LUKE](https://huggingface.co/docs/transformers/model_doc/luke), [MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm), [MEGA](https://huggingface.co/docs/transformers/model_doc/mega), [Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert), [MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert), [MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet), [MPT](https://huggingface.co/docs/transformers/model_doc/mpt), [MRA](https://huggingface.co/docs/transformers/model_doc/mra), [Nezha](https://huggingface.co/docs/transformers/model_doc/nezha), [Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer), [QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert), [RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert), [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert), [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer), [SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm), [XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet), [X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod), [YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)
- Text Generation: Mapping the unmasked_text to to the masked_text or privacy_mask attributes. Check out HuggingFace's [guide to fine-tunning](https://huggingface.co/docs/transformers/v4.15.0/training)
- [T5 Family](https://huggingface.co/docs/transformers/model_doc/t5), [Llama2](https://huggingface.co/docs/transformers/main/model_doc/llama2)
# Information regarding the rows:
- Each row represents a json object with a natural language text that includes placeholders for PII (and could plausibly be written by a human to an AI assistant).
- Sample row:
- "masked_text" contains a PII free natural text
- "Product officially launching in [COUNTY_1]. Estimate profit of [CURRENCYSYMBOL_1][AMOUNT_1]. Expenses by [ACCOUNTNAME_1].",
- "unmasked_text" shows a natural sentence containing PII
- "Product officially launching in Washington County. Estimate profit of $488293.16. Expenses by Checking Account."
- "privacy_mask" indicates the mapping between the privacy token instances and the string within the natural text.*
- "{'[COUNTY_1]': 'Washington County', '[CURRENCYSYMBOL_1]': '$', '[AMOUNT_1]': '488293.16', '[ACCOUNTNAME_1]': 'Checking Account'}"
- "span_labels" is an array of arrays formatted in the following way [start, end, pii token instance].*
- "[[0, 32, 'O'], [32, 49, 'COUNTY_1'], [49, 70, 'O'], [70, 71, 'CURRENCYSYMBOL_1'], [71, 80, 'AMOUNT_1'], [80, 94, 'O'], [94, 110, 'ACCOUNTNAME_1'], [110, 111, 'O']]",
- "bio_labels" follows the common place notation for "beginning", "inside" and "outside" of where each private tokens starts.[original paper](https://arxiv.org/abs/cmp-lg/9505040)
-["O", "O", "O", "O", "B-COUNTY", "I-COUNTY", "O", "O", "O", "O", "B-CURRENCYSYMBOL", "O", "O", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "O", "O", "O", "B-ACCOUNTNAME", "I-ACCOUNTNAME", "O"],
- "tokenised_text" breaks down the unmasked sentence into tokens using Bert Family tokeniser to help fine-tune large language models.
- ["product", "officially", "launching", "in", "washington", "county", ".", "estimate", "profit", "of", "$", "48", "##8", "##29", "##3", ".", "16", ".", "expenses", "by", "checking", "account", "."]
*note for the nested objects, we store them as string to maximise compability between various software.
# About Us:
At Ai4Privacy, we are commited to building the global seatbelt of the 21st century for Artificial Intelligence to help fight against potential risks of personal information being integrated into data pipelines.
Newsletter & updates: [www.Ai4Privacy.com](www.Ai4Privacy.com)
- Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages)
- Integrations with already existing open source solutions
- Ask us a question on discord: [https://discord.gg/kxSbJrUQZF](https://discord.gg/kxSbJrUQZF)
# Roadmap and Future Development
- Carbon Neutral
- Benchmarking
- Better multilingual and especially localisation
- Extended integrations
- Continuously increase the training set
- Further optimisation to the model to reduce size and increase generalisability
- Next released major update is planned for the 14th of December 2023 (subscribe to newsletter for updates)
# Use Cases and Applications
**Chatbots**: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses.
**Customer Support Systems**: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information.
**Email Filtering**: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information.
**Data Anonymization**: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes.
**Social Media Platforms**: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment.
**Content Moderation**: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details.
**Online Forms**: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection.
**Collaborative Document Editing**: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents.
**Research and Data Sharing**: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft.
**Content Generation**: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals.
(...and whatever else your creative mind can think of)
# Support and Maintenance
AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/). | [
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] |
bzantium/LITM | bzantium | 2023-12-12T01:46:34Z | 979 | 0 | [
"task_categories:question-answering",
"size_categories:n<1K",
"license:apache-2.0",
"lost-in-the-middle",
"arxiv:2307.03172",
"region:us"
] | [
"question-answering"
] | 2023-11-26T03:54:15Z | ---
license: apache-2.0
configs:
- config_name: kv75
data_files:
- split: test
path: "data/kv75.jsonl"
- config_name: kv140
data_files:
- split: test
path: "data/kv140.jsonl"
- config_name: kv300
data_files:
- split: test
path: "data/kv300.jsonl"
- config_name: qa10
data_files:
- split: test
path: "data/qa10.jsonl"
- config_name: qa20
data_files:
- split: test
path: "data/qa20.jsonl"
- config_name: qa30
data_files:
- split: test
path: "data/qa30.jsonl"
task_categories:
- question-answering
tags:
- lost-in-the-middle
size_categories:
- n<1K
---
# Datasets for Lost In The Middle
This repository contains datasets used in the paper ["Lost in the Middle: How Language Models Use Long Contexts"](https://arxiv.org/abs/2307.03172), focusing on multi-document question answering and key-value retrieval tasks.
## Datasets Overview
The datasets provided are as follows:
- **Key-Value Retrieval Datasets**
- `kv75`: Key-Value pairs with 75 keys.
- `kv140`: Key-Value pairs with 140 keys.
- `kv300`: Key-Value pairs with 300 keys.
- **Multi-Document Question Answering Datasets**
- `qa10`: Questions with answers derived from 10 documents.
- `qa20`: Questions with answers derived from 20 documents.
- `qa30`: Questions with answers derived from 30 documents.
## Loading the Data
You can load these datasets using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
### Example for loading the kv75 dataset
dataset = load_dataset("bzantium/LITM", "kv75")
### Example for loading the qa20 dataset
dataset = load_dataset("bzantium/LITM", "qa20")
``` | [
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nielsr/breast-cancer | nielsr | 2023-12-14T16:17:41Z | 954 | 6 | [
"region:us"
] | null | 2023-05-01T18:20:05Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 42431652.0
num_examples: 130
download_size: 0
dataset_size: 42431652.0
---
# Dataset Card for "breast-cancer"
Dataset was taken from the MedSAM project and used in [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) which fine-tunes Meta's SAM model on the dataset.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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llm-book/ner-wikipedia-dataset | llm-book | 2023-12-12T02:25:51Z | 667 | 0 | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:ja",
"license:cc-by-sa-3.0",
"region:us"
] | [
"token-classification"
] | 2023-04-15T10:43:21Z | ---
language:
- ja
license:
- cc-by-sa-3.0
size_categories:
- 1K<n<10K
task_categories:
- token-classification
---
# Dataset Card for llm-book/ner-wikipedia-dataset
書籍『大規模言語モデル入門』で使用する、ストックマーク株式会社により作成された「Wikipediaを用いた日本語の固有表現抽出データセット」(Version 2.0)です。
Githubリポジトリ[stockmarkteam/ner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)で公開されているデータセットを利用しています。
### Citation
```bibtex
@inproceedings{omi-2021-wikipedia,
title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築",
author = "近江 崇宏",
booktitle = "言語処理学会第27回年次大会",
year = "2021",
url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf",
}
```
### Licence
Wikipedia日本語版と同じCC-BY-SA 3.0のライセンスに従います。
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tomasg25/scientific_lay_summarisation | tomasg25 | 2023-12-12T07:51:52Z | 655 | 14 | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"abstractive-summarization",
"scientific-papers",
"lay-summarization",
"PLOS",
"eLife",
"arxiv:2210.09932",
"region:us"
] | [
"summarization"
] | 2022-10-19T14:46:52Z | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ScientificLaySummarisation
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
tags:
- abstractive-summarization
- scientific-papers
- lay-summarization
- PLOS
- eLife
task_categories:
- summarization
task_ids: []
---
# Dataset Card for "scientific_lay_summarisation"
- **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
- **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)
- **Size of downloaded dataset files:** 850.44 MB
- **Size of the generated dataset:** 1.32 GB
- **Total amount of disk used:** 2.17 GB
### Dataset Summary
This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
](https://arxiv.org/abs/2210.09932)" .
Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/analyses on the content of each dataset are provided in the paper.
Both "elife" and "plos" have 6 features:
- "article": the body of the document (including the abstract), sections separated by "/n".
- "section_headings": the title of each section, separated by "/n".
- "keywords": keywords describing the topic of the article, separated by "/n".
- "title": the title of the article.
- "year": the year the article was published.
- "summary": the lay summary of the document.
**Note:** The format of both datasets differs from that used in the original repository (given above) in order to make them compatible with the `run_summarization.py` script of Transformers. Specifically, sentence tokenization is removed via " ".join(text), and the abstract and article sections, previously lists of sentences, are combined into a single `string` feature ("article") with each section separated by "\n". For the sentence-tokenized version of the dataset, please use the original git repository.
### Supported Tasks and Leaderboards
Papers with code - [PLOS](https://paperswithcode.com/sota/lay-summarization-on-plos) and [eLife](https://paperswithcode.com/sota/lay-summarization-on-elife).
### Languages
English
## Dataset Structure
### Data Instances
#### plos
- **Size of downloaded dataset files:** 425.22 MB
- **Size of the generated dataset:** 1.05 GB
- **Total amount of disk used:** 1.47 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"summary": "In the kidney , structures known as nephrons are responsible for collecting metabolic waste . Nephrons are composed of a ...",
"article": "Kidney function depends on the nephron , which comprises a 'blood filter , a tubule that is subdivided into functionally ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and Methods'",
"keywords": "developmental biology\ndanio (zebrafish)\nvertebrates\nteleost fishes\nnephrology",
"title": "The cdx Genes and Retinoic Acid Control the Positioning and Segmentation of the Zebrafish Pronephros",
"year": "2007"
}
```
#### elife
- **Size of downloaded dataset files:** 425.22 MB
- **Size of the generated dataset:** 275.99 MB
- **Total amount of disk used:** 1.47 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"summary": "In the USA , more deaths happen in the winter than the summer . But when deaths occur varies greatly by sex , age , cause of ...",
"article": "In temperate climates , winter deaths exceed summer ones . However , there is limited information on the timing and the ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and methods",
"keywords": "epidemiology and global health",
"title": "National and regional seasonal dynamics of all-cause and cause-specific mortality in the USA from 1980 to 2016",
"year": "2018"
}
```
### Data Fields
The data fields are the same among all splits.
#### plos
- `article`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` feature.
#### elife
- `article`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name |train |validation|test|
|------|-----:|---------:|---:|
|plos | 24773| 1376|1376|
|elife | 4346| 241| 241|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
"Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature"
Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton
EMNLP 2022
``` | [
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visheratin/laion-coco-nllb | visheratin | 2023-12-14T15:18:01Z | 650 | 14 | [
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"license:cc-by-nc-4.0",
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] | [
"image-to-text",
"translation"
] | 2023-06-18T06:58:28Z | ---
language:
- ace
- acm
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- aeb
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- ajp
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- als
- am
- apc
- ar
- ars
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- umb
- ur
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- vec
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- zsm
- zu
license: cc-by-nc-4.0
size_categories:
- 100K<n<1M
task_categories:
- image-to-text
- translation
pretty_name: LAION-COCO translated to 200 languages
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: eng_caption
dtype: string
- name: captions
sequence:
sequence: string
- name: score
dtype: float64
splits:
- name: test
num_bytes: 271360114
num_examples: 14906
- name: train
num_bytes: 13371094087
num_examples: 735097
download_size: 8682991817
dataset_size: 13642454201
language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab,
aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng,
ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl,
bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn,
bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn,
dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn,
est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn,
fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr,
hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn,
ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn,
kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn,
kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn,
kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn,
lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn,
mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn,
mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn,
nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya,
pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn,
ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr,
sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn,
spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn,
szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi,
taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn,
twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn,
vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans,
zho_Hant, zul_Latn
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
---
# LAION COCO translated into 200 languages
This dataset contains the samples of the [LAION-COCO](https://huggingface.co/datasets/laion/laion-coco) dataset translated to 200 languages using
the largest [NLLB-200 model](https://huggingface.co/facebook/nllb-200-3.3B) (3.3B parameters).
## Fields description
1. `id` - unique ID of the image.
2. `url` - original URL of the image from the LAION-COCO dataset.
3. `eng_caption` - original English caption from the LAION-COCO dataset.
4. `captions` - a list of captions translated to the languages from the Flores 200 dataset. Every item in the list is a list where the first element is a BCP-47 language code, and the second one is a caption in this language. The list of all language codes for the Flores 200 dataset can be found [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200).
5. `score` - aesthetic score generated using [LAION aesthetic predictor](https://github.com/christophschuhmann/improved-aesthetic-predictor/). The images in the dataset have the score of 4.5+.
## Images
The dataset was filtered to contain only working image URLs. However, the availability may change in the future. Because of that, all images from this dataset are available at [https://nllb-data.com/](https://nllb-data.com/).
To get the image, use the following format:
```
https://nllb-data.com/{id}.jpg
```
## Paper
The dataset was used to train the models in the paper: "[NLLB-CLIP - train performant multilingual image retrieval model on a budget](https://arxiv.org/abs/2309.01859)". | [
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] |
kira/math-dpo | kira | 2023-12-13T12:25:28Z | 641 | 0 | [
"region:us"
] | null | 2023-12-13T12:21:38Z | ---
dataset_info:
features:
- name: metadata
dtype: string
id: metadata
- name: chosen_rating
dtype: float64
- name: rejected_rating
dtype: float64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 6976299.638544251
num_examples: 2393
- name: test
num_bytes: 72882.36145574856
num_examples: 25
download_size: 3135711
dataset_size: 7049182.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
```
DatasetDict({
train: Dataset({
features: ['metadata', 'chosen_rating', 'rejected_rating', 'prompt', 'chosen', 'rejected'],
num_rows: 2393
})
test: Dataset({
features: ['metadata', 'chosen_rating', 'rejected_rating', 'prompt', 'chosen', 'rejected'],
num_rows: 25
})
})
```
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pufanyi/MIMICIT | pufanyi | 2023-12-13T04:16:00Z | 606 | 20 | [
"size_categories:1M<n<10M",
"language:en",
"language:zh",
"language:es",
"language:ja",
"language:fr",
"language:ko",
"language:ar",
"license:mit",
"arxiv:2306.05425",
"region:us"
] | null | 2023-07-12T07:22:42Z | ---
license: mit
language:
- en
- zh
- es
- ja
- fr
- ko
- ar
arxiv: 2306.05425
extra_gated_prompt: |
<h1>MIMIC-IT Dataset Download
Agreement</h1>
<p>S-Lab, Nanyang Technological University (S-Lab) provides access to
the MIMIC-IT Dataset (referred to as the Dataset) under the following
conditions.</p>
<p>By signing, the researcher agrees to the following terms of use:</p>
<ol type="1">
<li>S-Lab makes no warranties regarding the Dataset, including but not
limited to being up-to-date, correct or complete. S-Lab cannot be held
liable for providing access to the Dataset or usage of the Dataset.</li>
<li>The Dataset should only be used for scientific or research purposes.
Any other use is explicitly prohibited.</li>
<li>The researcher agrees to the following terms and conditions of data
sources of the Dataset:
<ul>
<li>TVC: <a href="https://tvqa.cs.unc.edu/">https://tvqa.cs.unc.edu/</a></li>
<li>LLaVA: <a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K">https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K</a>; <a href="https://cocodataset.org/#termsofuse">https://cocodataset.org/#termsofuse</a></li>
<li>DC: <a href="http://activity-net.org/index.html">http://activity-net.org/index.html</a></li>
<li>VIST: <a href="https://visionandlanguage.net/VIST/index.html">https://visionandlanguage.net/VIST/index.html</a></li>
<li>SD: <a href="https://goo.gl/forms/HJiFJSllupqeCbax1">https://goo.gl/forms/HJiFJSllupqeCbax1</a></li>
<li>Ego4D: <a href="https://ego4ddataset.com/">https://ego4ddataset.com/</a></li>
</ul></li>
<li>The researcher takes full responsibility for usage of the Dataset at
any time.</li>
<li>S-Lab reserves the right to terminate the researcher's access to the
Dataset at any time.</li>
<li>The place of jurisdiction is Singapore.</li>
<li>If any part of this agreement is legally invalid, this shall not
affect the remaining agreement.</li>
</ol>
extra_gated_fields:
Verifiable Name: text
Institution Email: text
Institutional Affiliation: text
I agree with the agreement: checkbox
pretty_name: 'MIMIC-IT: Multi-Modal In-Context Instruction Tuning'
size_categories:
- 1M<n<10M
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/sxy8v9PS/mimicit-logo.png" width="80%" height="80%">
</p>
<div>
<div align="center">
<a href='https://brianboli.com/' target='_blank'>Bo Li<sup>*,♠,1</sup></a> 
<a href='https://zhangyuanhan-ai.github.io/' target='_blank'>Yuanhan Zhang<sup>*,♠,1</sup></a> 
<a href='https://cliangyu.com/' target='_blank'>Liangyu Chen<sup>*,1</sup></a> 
<a href='https://king159.github.io/' target='_blank'>Jinghao Wang<sup>*,1</sup></a> 
<a href='https://pufanyi.github.io/' target='_blank'>Fanyi Pu<sup>*,1</sup></a> 
</br>
<a href='https://jingkang50.github.io/' target='_blank'>Jingkang Yang<sup>1</sup></a> 
<a href='https://chunyuan.li/' target='_blank'>Chunyuan Li<sup>2</sup></a> 
<a href='https://liuziwei7.github.io/' target='_blank'>Ziwei Liu<sup>✉,1</sup></a>
</div>
<div>
<div align="center">
<sup>1</sup>S-Lab, Nanyang Technological University 
<sup>2</sup>Microsoft Research, Redmond
</br>
<sup>♠</sup> Co-Project Lead 
<sup>*</sup> Equal Contribution 
<sup>✉</sup> Corresponding Author
</div>
## Dataset Description
- **Homepage: https://otter-ntu.github.io**
- **Repository: https://github.com/Luodian/Otter**
- **Paper: https://arxiv.org/abs/2306.05425**
**Note 1: To reduce memory consumption during image loading and improve loading speed, we are converting the JSON format of images to the Parquet format. For detailed information, please refer to [this link](https://github.com/Luodian/Otter/blob/main/docs/mimicit_format.md).**
**Note 2: We are uploading the full version of `DC` and `E4D`, the new files are indicated by the suffix `1207`.**
### Dataset Summary
MIMIC-IT offers a diverse and extensive dataset of 2.8M multimodal instruction-response pairs, designed to enhance the performance of Vision-Language Models (VLMs) in real-life scenarios, enabling VLMs to excel in perception, reasoning, and planning while also catering to a multilingual audience.
MIMIC-IT enables the application of egocentric visual assistant model that can serve that can answer your questions like **Hey, Do you think I left my keys on the table?**. Harness the power of MIMIC-IT to unlock the full potential of your AI-driven visual assistant and elevate your interactive vision-language tasks to new heights.
MIMIC-IT provides multilingual instructions, supporting English, Chinese, Korean, Japanese, German, French, Spanish, and Arabic, thereby allowing a larger global audience to altogether enjoy from the convenience brought about by advancements in artificial intelligence.
<p align="center" width="100%">
<img src="https://i.postimg.cc/4x66gHhw/mimic-it.jpg" width="100%" height="100%">
</p>
## Using MIMIC-IT
We have already upload the `images.parquet` file. You can check [`tools/load.py`](tools/load.py) to learn how to load the dataset (`instruction.json` + `images.parquet`) and check the integrity of the whole dataset.
You can also use [this code](https://huggingface.co/datasets/pufanyi/MIMICIT/blob/main/tools/convert_to_parquet.py) to convert `image.json` to `parquet` version by yourself.
You can following the steps to obtain the MIMIC-IT dataset. Each task (e.g. `DC`, `LA`) in MIMIC-IT is composed of three parts, including:
1. `xx.json` file: the images in base64 format.
2. `xx_instructions.json` file: the instruction-response pairs (also includes image ids and related instructions ids for each instruction-response pair) for each task.
3. `xx_train.json` file: the customized related instruction-response pairs for each instruction.
You can directly download the contents in the `data` folder. The distribution of the `data` folder is as follows:
```plain
data/
CGD/
CGD.json
CGD_images_preview.csv
CGD_instructions.json
...
```
For each `dataset_name`, there are three main files **except for `DC` and `E4D`**:
1. `{dataset_name}.json`: Stores the image numbers and their corresponding base64 codes in lossless compressed PNG format.
```json
{
"image_id_1": "base64_code_1",
"image_id_2": "base64_code_2",
...
}
```
2. `{dataset_name}_images_preview.csv`: Stores the image numbers and their corresponding base64 codes in lossy compressed JPG format, mainly used for display in the Dataset Card.
```csv
id, image
"image_id_1", "base64_code_1"
"image_id_2", "base64_code_2"
...
```
3. `{dataset_name}_instructions.json`: Stores each instruction and its associated answer.
```json
{
"meta": {
"version": current_version,
"time": update_time,
"author": "ntu"
},
"data": {
"instruction_id_1": {
"instruction": "instruction_1",
"answer": "answer_of_instruction_1",
"image_ids": [
"image_id_1",
"image_id_2",
...
],
"rel_ins_ids": [
"related_instruction_id_1",
"related_instruction_id_2",
...
]
},
...
}
}
```
Of course, you can also use `wget` or `curl` for direct downloads. Below is an example.
Before proceeding with the downloads, you need to set your Hugging Face token. For that, please refer to [this page](https://huggingface.co/docs/hub/security-tokens).
```shell
$ # Set Hugging Face Token
$ HF_TOKEN="YOUR_HUGGING_FACE_TOKEN"
$ # Set the dataset you want to download
$ DATASET_NAME="DATASET_YOU_WANT_TO_DOWNLOAD" # e.g. CGD
$ # Download {DATASET_NAME}.json
$ wget --header="Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}.json"
$ # Download {DATASET_NAME}_instructions.json
$ wget --header="Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_instructions.json"
$ # Download {DATASET_NAME}_images_preview.csv (usually not necessary)
$ wget --header="Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_images_preview.csv"
```
Or
```shell
$ # Set Hugging Face Token
$ HF_TOKEN="YOUR_HUGGING_FACE_TOKEN"
$ # Set the dataset you want to download
$ DATASET_NAME="DATASET_YOU_WANT_TO_DOWNLOAD" # e.g. CGD
$ # Download {DATASET_NAME}.json
$ curl -LJO -H "Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}.json"
$ # Download {DATASET_NAME}_instructions.json
$ curl -LJO -H "Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_instructions.json"
$ # Download {DATASET_NAME}_images_preview.csv (usually not necessary)
$ curl -LJO -H "Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_images_preview.csv"
```
Alternatively, you can use `dataset.load_dataset` for downloading. However, due to Hugging Face's size limitations, all images can only be loaded in JPG format. Below is an example using `CGD` dataset:
### CGD_Images
Download the JPG format images and their corresponding identifiers:
```python
from datasets import load_dataset
data = load_dataset("pufanyi/MIMICIT", "CGD_Images")
```
The format will be like:
```json
{
"id": "CGD_IMG_000000426149",
"image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7F84601D62F0>
}
```
It should be noted that, due to size limitations, for `DC` (Dense Captions), this command will only extract a portion of the images from the `DC` collection for downloading.
### CGD_Instructions
Download all instructions:
```python
from datasets import load_dataset
data = load_dataset("pufanyi/MIMICIT", "CGD_Instructions")
```
The format will be like:
```json
{
"id": "CGD_INS_000000",
"instruction": "What is the difference between the two pizzas in these images?",
"answer": "The pizza in the first image is on a red plate and being held by an old lady, while the pizza in the second image is on a metal counter being prepared by a woman in a blue shirt.",
"images": [
"CGD_IMG_000000069568",
"CGD_IMG_000000328270"
],
"related instructions": [
"CGD_INS_000001"
]
}
```
### CGD_Preview
Download all instructions along with their corresponding JPG images:
```python
from datasets import load_dataset
data = load_dataset("pufanyi/MIMICIT", "CGD_Preview")
```
The format will be like:
```json
{
"id": "CGD_INS_000000",
"instruction": "What is the difference between the two pizzas in these images?",
"answer": "The pizza in the first image is on a red plate and being held by an old lady, while the pizza in the second image is on a metal counter being prepared by a woman in a blue shirt.",
"images": [
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7F8460267DF0>,
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7F8460267700>
],
"related instructions": [
"CGD_INS_000001"
]
}
```
It should be noted that, due to size limitations, for `DC` (Dense Captions), this command will only extract a portion of the images from the `DC` collection for downloading. | [
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jat-project/jat-dataset-tokenized | jat-project | 2023-12-17T21:20:21Z | 579 | 0 | [
"region:us"
] | null | 2023-12-16T10:10:31Z | ---
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- config_name: atari-namethisgame
data_files:
- split: train
path: atari-namethisgame/train-*
- split: test
path: atari-namethisgame/test-*
- config_name: atari-phoenix
data_files:
- split: train
path: atari-phoenix/train-*
- split: test
path: atari-phoenix/test-*
- config_name: atari-pitfall
data_files:
- split: train
path: atari-pitfall/train-*
- split: test
path: atari-pitfall/test-*
- config_name: atari-pong
data_files:
- split: test
path: atari-pong/test-*
- split: train
path: atari-pong/train-*
- config_name: atari-privateeye
data_files:
- split: test
path: atari-privateeye/test-*
- split: train
path: atari-privateeye/train-*
- config_name: atari-qbert
data_files:
- split: test
path: atari-qbert/test-*
- split: train
path: atari-qbert/train-*
- config_name: atari-riverraid
data_files:
- split: test
path: atari-riverraid/test-*
- split: train
path: atari-riverraid/train-*
- config_name: atari-roadrunner
data_files:
- split: test
path: atari-roadrunner/test-*
- split: train
path: atari-roadrunner/train-*
- config_name: atari-robotank
data_files:
- split: test
path: atari-robotank/test-*
- split: train
path: atari-robotank/train-*
- config_name: babyai-action-obj-door
data_files:
- split: train
path: babyai-action-obj-door/train-*
- split: test
path: babyai-action-obj-door/test-*
- config_name: babyai-blocked-unlock-pickup
data_files:
- split: test
path: babyai-blocked-unlock-pickup/test-*
- split: train
path: babyai-blocked-unlock-pickup/train-*
- config_name: babyai-boss-level
data_files:
- split: test
path: babyai-boss-level/test-*
- split: train
path: babyai-boss-level/train-*
- config_name: babyai-boss-level-no-unlock
data_files:
- split: test
path: babyai-boss-level-no-unlock/test-*
- split: train
path: babyai-boss-level-no-unlock/train-*
- config_name: babyai-find-obj-s5
data_files:
- split: train
path: babyai-find-obj-s5/train-*
- split: test
path: babyai-find-obj-s5/test-*
- config_name: babyai-go-to
data_files:
- split: train
path: babyai-go-to/train-*
- split: test
path: babyai-go-to/test-*
- config_name: babyai-go-to-door
data_files:
- split: train
path: babyai-go-to-door/train-*
- split: test
path: babyai-go-to-door/test-*
- config_name: babyai-go-to-imp-unlock
data_files:
- split: train
path: babyai-go-to-imp-unlock/train-*
- split: test
path: babyai-go-to-imp-unlock/test-*
- config_name: babyai-go-to-local
data_files:
- split: train
path: babyai-go-to-local/train-*
- split: test
path: babyai-go-to-local/test-*
- config_name: babyai-go-to-obj
data_files:
- split: train
path: babyai-go-to-obj/train-*
- split: test
path: babyai-go-to-obj/test-*
- config_name: babyai-go-to-obj-door
data_files:
- split: train
path: babyai-go-to-obj-door/train-*
- split: test
path: babyai-go-to-obj-door/test-*
- config_name: babyai-go-to-red-ball
data_files:
- split: train
path: babyai-go-to-red-ball/train-*
- split: test
path: babyai-go-to-red-ball/test-*
- config_name: babyai-go-to-red-ball-grey
data_files:
- split: train
path: babyai-go-to-red-ball-grey/train-*
- split: test
path: babyai-go-to-red-ball-grey/test-*
- config_name: babyai-go-to-red-ball-no-dists
data_files:
- split: train
path: babyai-go-to-red-ball-no-dists/train-*
- split: test
path: babyai-go-to-red-ball-no-dists/test-*
- config_name: babyai-go-to-red-blue-ball
data_files:
- split: train
path: babyai-go-to-red-blue-ball/train-*
- split: test
path: babyai-go-to-red-blue-ball/test-*
- config_name: babyai-go-to-seq
data_files:
- split: train
path: babyai-go-to-seq/train-*
- split: test
path: babyai-go-to-seq/test-*
- config_name: babyai-key-corridor
data_files:
- split: test
path: babyai-key-corridor/test-*
- split: train
path: babyai-key-corridor/train-*
- config_name: babyai-mini-boss-level
data_files:
- split: test
path: babyai-mini-boss-level/test-*
- split: train
path: babyai-mini-boss-level/train-*
- config_name: babyai-move-two-across-s8n9
data_files:
- split: test
path: babyai-move-two-across-s8n9/test-*
- split: train
path: babyai-move-two-across-s8n9/train-*
- config_name: babyai-one-room-s8
data_files:
- split: test
path: babyai-one-room-s8/test-*
- split: train
path: babyai-one-room-s8/train-*
- config_name: babyai-open
data_files:
- split: test
path: babyai-open/test-*
- split: train
path: babyai-open/train-*
- config_name: babyai-open-door
data_files:
- split: test
path: babyai-open-door/test-*
- split: train
path: babyai-open-door/train-*
- config_name: babyai-open-doors-order-n4
data_files:
- split: test
path: babyai-open-doors-order-n4/test-*
- split: train
path: babyai-open-doors-order-n4/train-*
- config_name: babyai-open-red-door
data_files:
- split: test
path: babyai-open-red-door/test-*
- split: train
path: babyai-open-red-door/train-*
- config_name: babyai-open-two-doors
data_files:
- split: test
path: babyai-open-two-doors/test-*
- split: train
path: babyai-open-two-doors/train-*
- config_name: babyai-pickup
data_files:
- split: test
path: babyai-pickup/test-*
- split: train
path: babyai-pickup/train-*
- config_name: babyai-pickup-above
data_files:
- split: test
path: babyai-pickup-above/test-*
- split: train
path: babyai-pickup-above/train-*
- config_name: babyai-pickup-dist
data_files:
- split: test
path: babyai-pickup-dist/test-*
- split: train
path: babyai-pickup-dist/train-*
- config_name: babyai-pickup-loc
data_files:
- split: test
path: babyai-pickup-loc/test-*
- split: train
path: babyai-pickup-loc/train-*
- config_name: babyai-put-next
data_files:
- split: train
path: babyai-put-next/train-*
- split: test
path: babyai-put-next/test-*
- config_name: babyai-put-next-local
data_files:
- split: train
path: babyai-put-next-local/train-*
- split: test
path: babyai-put-next-local/test-*
- config_name: babyai-synth-loc
data_files:
- split: test
path: babyai-synth-loc/test-*
- split: train
path: babyai-synth-loc/train-*
- config_name: babyai-synth-seq
data_files:
- split: test
path: babyai-synth-seq/test-*
- split: train
path: babyai-synth-seq/train-*
- config_name: babyai-unblock-pickup
data_files:
- split: test
path: babyai-unblock-pickup/test-*
- split: train
path: babyai-unblock-pickup/train-*
- config_name: babyai-unlock
data_files:
- split: train
path: babyai-unlock/train-*
- split: test
path: babyai-unlock/test-*
- config_name: babyai-unlock-local
data_files:
- split: test
path: babyai-unlock-local/test-*
- split: train
path: babyai-unlock-local/train-*
- config_name: babyai-unlock-pickup
data_files:
- split: test
path: babyai-unlock-pickup/test-*
- split: train
path: babyai-unlock-pickup/train-*
- config_name: babyai-unlock-to-unlock
data_files:
- split: train
path: babyai-unlock-to-unlock/train-*
- split: test
path: babyai-unlock-to-unlock/test-*
- config_name: conceptual-captions
data_files:
- split: test
path: conceptual-captions/test-*
- split: train
path: conceptual-captions/train-*
- config_name: metaworld-assembly
data_files:
- split: train
path: metaworld-assembly/train-*
- split: test
path: metaworld-assembly/test-*
- config_name: metaworld-basketball
data_files:
- split: train
path: metaworld-basketball/train-*
- split: test
path: metaworld-basketball/test-*
- config_name: metaworld-bin-picking
data_files:
- split: train
path: metaworld-bin-picking/train-*
- split: test
path: metaworld-bin-picking/test-*
- config_name: metaworld-box-close
data_files:
- split: train
path: metaworld-box-close/train-*
- split: test
path: metaworld-box-close/test-*
- config_name: metaworld-button-press
data_files:
- split: train
path: metaworld-button-press/train-*
- split: test
path: metaworld-button-press/test-*
- config_name: metaworld-button-press-topdown
data_files:
- split: train
path: metaworld-button-press-topdown/train-*
- split: test
path: metaworld-button-press-topdown/test-*
- config_name: metaworld-button-press-topdown-wall
data_files:
- split: train
path: metaworld-button-press-topdown-wall/train-*
- split: test
path: metaworld-button-press-topdown-wall/test-*
- config_name: metaworld-button-press-wall
data_files:
- split: train
path: metaworld-button-press-wall/train-*
- split: test
path: metaworld-button-press-wall/test-*
- config_name: metaworld-coffee-button
data_files:
- split: train
path: metaworld-coffee-button/train-*
- split: test
path: metaworld-coffee-button/test-*
- config_name: metaworld-coffee-pull
data_files:
- split: train
path: metaworld-coffee-pull/train-*
- split: test
path: metaworld-coffee-pull/test-*
- config_name: metaworld-coffee-push
data_files:
- split: train
path: metaworld-coffee-push/train-*
- split: test
path: metaworld-coffee-push/test-*
- config_name: metaworld-dial-turn
data_files:
- split: train
path: metaworld-dial-turn/train-*
- split: test
path: metaworld-dial-turn/test-*
- config_name: metaworld-disassemble
data_files:
- split: train
path: metaworld-disassemble/train-*
- split: test
path: metaworld-disassemble/test-*
- config_name: metaworld-door-close
data_files:
- split: train
path: metaworld-door-close/train-*
- split: test
path: metaworld-door-close/test-*
- config_name: metaworld-door-lock
data_files:
- split: train
path: metaworld-door-lock/train-*
- split: test
path: metaworld-door-lock/test-*
- config_name: metaworld-door-open
data_files:
- split: train
path: metaworld-door-open/train-*
- split: test
path: metaworld-door-open/test-*
- config_name: metaworld-door-unlock
data_files:
- split: train
path: metaworld-door-unlock/train-*
- split: test
path: metaworld-door-unlock/test-*
- config_name: metaworld-drawer-close
data_files:
- split: train
path: metaworld-drawer-close/train-*
- split: test
path: metaworld-drawer-close/test-*
- config_name: metaworld-drawer-open
data_files:
- split: train
path: metaworld-drawer-open/train-*
- split: test
path: metaworld-drawer-open/test-*
- config_name: metaworld-faucet-close
data_files:
- split: train
path: metaworld-faucet-close/train-*
- split: test
path: metaworld-faucet-close/test-*
- config_name: metaworld-faucet-open
data_files:
- split: train
path: metaworld-faucet-open/train-*
- split: test
path: metaworld-faucet-open/test-*
- config_name: metaworld-hammer
data_files:
- split: train
path: metaworld-hammer/train-*
- split: test
path: metaworld-hammer/test-*
- config_name: metaworld-hand-insert
data_files:
- split: train
path: metaworld-hand-insert/train-*
- split: test
path: metaworld-hand-insert/test-*
- config_name: metaworld-handle-press
data_files:
- split: train
path: metaworld-handle-press/train-*
- split: test
path: metaworld-handle-press/test-*
- config_name: metaworld-handle-press-side
data_files:
- split: train
path: metaworld-handle-press-side/train-*
- split: test
path: metaworld-handle-press-side/test-*
- config_name: metaworld-handle-pull
data_files:
- split: train
path: metaworld-handle-pull/train-*
- split: test
path: metaworld-handle-pull/test-*
- config_name: metaworld-handle-pull-side
data_files:
- split: train
path: metaworld-handle-pull-side/train-*
- split: test
path: metaworld-handle-pull-side/test-*
- config_name: metaworld-lever-pull
data_files:
- split: train
path: metaworld-lever-pull/train-*
- split: test
path: metaworld-lever-pull/test-*
- config_name: metaworld-peg-insert-side
data_files:
- split: train
path: metaworld-peg-insert-side/train-*
- split: test
path: metaworld-peg-insert-side/test-*
- config_name: metaworld-peg-unplug-side
data_files:
- split: train
path: metaworld-peg-unplug-side/train-*
- split: test
path: metaworld-peg-unplug-side/test-*
- config_name: metaworld-pick-out-of-hole
data_files:
- split: train
path: metaworld-pick-out-of-hole/train-*
- split: test
path: metaworld-pick-out-of-hole/test-*
- config_name: metaworld-pick-place
data_files:
- split: train
path: metaworld-pick-place/train-*
- split: test
path: metaworld-pick-place/test-*
- config_name: metaworld-pick-place-wall
data_files:
- split: train
path: metaworld-pick-place-wall/train-*
- split: test
path: metaworld-pick-place-wall/test-*
- config_name: metaworld-plate-slide
data_files:
- split: train
path: metaworld-plate-slide/train-*
- split: test
path: metaworld-plate-slide/test-*
- config_name: metaworld-plate-slide-back
data_files:
- split: train
path: metaworld-plate-slide-back/train-*
- split: test
path: metaworld-plate-slide-back/test-*
- config_name: metaworld-plate-slide-back-side
data_files:
- split: train
path: metaworld-plate-slide-back-side/train-*
- split: test
path: metaworld-plate-slide-back-side/test-*
- config_name: metaworld-plate-slide-side
data_files:
- split: train
path: metaworld-plate-slide-side/train-*
- split: test
path: metaworld-plate-slide-side/test-*
- config_name: metaworld-push
data_files:
- split: train
path: metaworld-push/train-*
- split: test
path: metaworld-push/test-*
- config_name: metaworld-push-back
data_files:
- split: train
path: metaworld-push-back/train-*
- split: test
path: metaworld-push-back/test-*
- config_name: metaworld-push-wall
data_files:
- split: train
path: metaworld-push-wall/train-*
- split: test
path: metaworld-push-wall/test-*
- config_name: metaworld-reach
data_files:
- split: train
path: metaworld-reach/train-*
- split: test
path: metaworld-reach/test-*
- config_name: metaworld-reach-wall
data_files:
- split: train
path: metaworld-reach-wall/train-*
- split: test
path: metaworld-reach-wall/test-*
- config_name: metaworld-shelf-place
data_files:
- split: train
path: metaworld-shelf-place/train-*
- split: test
path: metaworld-shelf-place/test-*
- config_name: metaworld-soccer
data_files:
- split: train
path: metaworld-soccer/train-*
- split: test
path: metaworld-soccer/test-*
- config_name: metaworld-stick-pull
data_files:
- split: train
path: metaworld-stick-pull/train-*
- split: test
path: metaworld-stick-pull/test-*
- config_name: metaworld-stick-push
data_files:
- split: train
path: metaworld-stick-push/train-*
- split: test
path: metaworld-stick-push/test-*
- config_name: metaworld-sweep
data_files:
- split: train
path: metaworld-sweep/train-*
- split: test
path: metaworld-sweep/test-*
- config_name: metaworld-sweep-into
data_files:
- split: train
path: metaworld-sweep-into/train-*
- split: test
path: metaworld-sweep-into/test-*
- config_name: metaworld-window-close
data_files:
- split: train
path: metaworld-window-close/train-*
- split: test
path: metaworld-window-close/test-*
- config_name: metaworld-window-open
data_files:
- split: train
path: metaworld-window-open/train-*
- split: test
path: metaworld-window-open/test-*
- config_name: mujoco-ant
data_files:
- split: train
path: mujoco-ant/train-*
- split: test
path: mujoco-ant/test-*
- config_name: mujoco-doublependulum
data_files:
- split: train
path: mujoco-doublependulum/train-*
- split: test
path: mujoco-doublependulum/test-*
- config_name: mujoco-halfcheetah
data_files:
- split: train
path: mujoco-halfcheetah/train-*
- split: test
path: mujoco-halfcheetah/test-*
- config_name: mujoco-hopper
data_files:
- split: train
path: mujoco-hopper/train-*
- split: test
path: mujoco-hopper/test-*
- config_name: mujoco-humanoid
data_files:
- split: train
path: mujoco-humanoid/train-*
- split: test
path: mujoco-humanoid/test-*
- config_name: mujoco-pendulum
data_files:
- split: train
path: mujoco-pendulum/train-*
- split: test
path: mujoco-pendulum/test-*
- config_name: mujoco-pusher
data_files:
- split: train
path: mujoco-pusher/train-*
- split: test
path: mujoco-pusher/test-*
- config_name: mujoco-reacher
data_files:
- split: train
path: mujoco-reacher/train-*
- split: test
path: mujoco-reacher/test-*
- config_name: mujoco-standup
data_files:
- split: train
path: mujoco-standup/train-*
- split: test
path: mujoco-standup/test-*
- config_name: mujoco-swimmer
data_files:
- split: train
path: mujoco-swimmer/train-*
- split: test
path: mujoco-swimmer/test-*
- config_name: mujoco-walker
data_files:
- split: train
path: mujoco-walker/train-*
- split: test
path: mujoco-walker/test-*
- config_name: ok-vqa
data_files:
- split: train
path: ok-vqa/train-*
- split: test
path: ok-vqa/test-*
- config_name: oscar
data_files:
- split: train
path: oscar/train-*
- split: test
path: oscar/test-*
- config_name: wikipedia
data_files:
- split: train
path: wikipedia/train-*
- split: test
path: wikipedia/test-*
---
# Dataset Card for "jat-dataset-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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minimario/livecodebench-execute-v2 | minimario | 2023-12-17T03:57:09Z | 567 | 0 | [
"region:us"
] | null | 2023-12-17T03:44:26Z | ---
dataset_info:
features:
- name: id
sequence: int64
- name: function_name
dtype: string
- name: code
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: test
num_bytes: 838050
num_examples: 2069
download_size: 124406
dataset_size: 838050
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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ktrinh38/fashion-dataset | ktrinh38 | 2023-12-11T18:10:18Z | 538 | 0 | [
"region:us"
] | null | 2023-12-11T18:02:50Z | ---
dataset_info:
features:
- name: id
dtype: int32
- name: brandName
dtype: string
- name: ageGroup
dtype: string
- name: gender
dtype: string
- name: baseColour
dtype: string
- name: season
dtype: string
- name: usage
dtype: string
- name: displayCategories
dtype: string
- name: productDisplayName
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 20771830465.821
num_examples: 44441
download_size: 12665598994
dataset_size: 20771830465.821
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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argilla/ultrafeedback-binarized-preferences-cleaned | argilla | 2023-12-11T14:22:19Z | 536 | 9 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"dpo",
"preference",
"ultrafeedback",
"region:us"
] | [
"text-generation"
] | 2023-12-05T11:07:34Z | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-generation
pretty_name: UltraFeedback Binarized Preferences Cleaned
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected-rating
dtype: float64
- name: rejected-model
dtype: string
splits:
- name: train
num_bytes: 284937773
num_examples: 60917
download_size: 143257393
dataset_size: 284937773
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- dpo
- preference
- ultrafeedback
---
# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/argilla/ultrafeedback-binarized-preferences),
and is the **recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback**.
Read more about Argilla's approach towards UltraFeedback binarization at [`argilla/ultrafeedback-binarized-preferences/README.md`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences/blob/main/README.md).
## Differences with `argilla/ultrafeedback-binarized-preferences`
Thanks to the recent issue identified by [AllenAI](https://huggingface.co/allenai) related to the TruthfulQA contamination within the
original UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking
in the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) from HuggingFace H4), we also decided
to follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which
implied using the average of the preference ratings rather than the critique overall score, as
[`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) did.
Besides that, we also saw that not only the rows with the `source=truthful_qa` were contamined (for obvious reasons), but also some
coming from ShareGPT, so we also removed those doing a left join with both subsets from the [`truthful_qa`](https://huggingface.co/datasets/truthful_qa) dataset.
Additionally, we also modified the formatting to be aligned with both [`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized),
and [`allenai/ultrafeedback_binarized_cleaned`](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) in order to ease
the integration within the [`huggingface/alignment-handbook`](https://github.com/huggingface/alignment-handbook) so that the formatting is standardized.
## Reproduce
<a target="_blank" href="https://colab.research.google.com/drive/1XR9P1St4yTNY0tjti_tIjm-yzP5Bfqc0?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
To reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to
remove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at [`notebook.ipynb`](./notebook.ipynb) within this repository.
From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as
ourselves, as well as the whole community, benefit a lot from open source and we also want to give back.
## Citation
If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: https://huggingface.co/datasets/openbmb/UltraFeedback
Additionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset:
```bibtex
@misc{notus2023,
author = {Alvaro Bartolome and Gabriel Martin and Daniel Vila},
title = {Notus},
year = {2023},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/argilla-io/notus}}
}
```
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] |
llm-book/livedoor-news-corpus | llm-book | 2023-12-12T02:19:43Z | 503 | 3 | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:ja",
"news",
"region:us"
] | [
"summarization"
] | 2023-06-21T07:16:52Z | ---
task_categories:
- summarization
language:
- ja
tags:
- news
pretty_name: livedoor-news-corpus
size_categories:
- 1K<n<10K
---
# Dataset Card for llm-book/ner-wikinews-dataset
書籍『大規模言語モデル入門』で使用する、株式会社ロンウイットが提供する「livedoorニュースコーパス」によるデータセットです。
[オリジナルのサイト](https://www.rondhuit.com/download.html)と同じものを使用しています。
本コーパスは、NHN Japan株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限りHTMLタグを取り除いて作成したものです。
### Licence
Attribution-NoDerivs 2.1 Japan (CC BY-ND 2.1 JP) License | [
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yashraizada/yelp-open-dataset-top-businesses | yashraizada | 2023-12-12T04:30:03Z | 490 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-09T19:19:49Z | ---
license: apache-2.0
---
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jinaai/code_search_net_clean | jinaai | 2023-12-11T10:42:18Z | 460 | 0 | [
"region:us"
] | null | 2023-11-15T17:58:52Z | ---
dataset_info:
features:
- name: doc
dtype: string
- name: code
dtype: string
splits:
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num_bytes: 196497807
num_examples: 311516
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num_bytes: 404530092
num_examples: 417492
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- name: test.ruby
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num_bytes: 20661926
num_examples: 22931
- name: validation.python
num_bytes: 24904263
num_examples: 21955
- name: validation.ruby
num_bytes: 1504017
num_examples: 2110
download_size: 632494450
dataset_size: 1753788357
configs:
- config_name: default
data_files:
- split: train.go
path: data/train.go-*
- split: train.java
path: data/train.java-*
- split: train.javascript
path: data/train.javascript-*
- split: train.php
path: data/train.php-*
- split: train.python
path: data/train.python-*
- split: train.ruby
path: data/train.ruby-*
- split: test.go
path: data/test.go-*
- split: test.java
path: data/test.java-*
- split: test.javascript
path: data/test.javascript-*
- split: test.php
path: data/test.php-*
- split: test.python
path: data/test.python-*
- split: test.ruby
path: data/test.ruby-*
- split: validation.go
path: data/validation.go-*
- split: validation.java
path: data/validation.java-*
- split: validation.javascript
path: data/validation.javascript-*
- split: validation.php
path: data/validation.php-*
- split: validation.python
path: data/validation.python-*
- split: validation.ruby
path: data/validation.ruby-*
---
# Dataset Card for "code_search_net_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Siki-77/twitter2017 | Siki-77 | 2023-12-17T09:03:49Z | 428 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-11T14:18:59Z | ---
license: apache-2.0
---
### dataset source
excellent sharing:
https://github.com/cbaziotis/datastories-semeval2017-task4/tree/master/dataset/Subtask_A/4A-English
task government:
https://alt.qcri.org/semeval2017/task4/index.php?id=data-and-tools | [
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Siki-77/amazon6_polarity | Siki-77 | 2023-12-17T08:40:51Z | 413 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T12:59:37Z | ---
license: apache-2.0
---
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] |
gmongaras/BERT_Base_Cased_512_GLUE_Mapped | gmongaras | 2023-12-11T20:34:53Z | 394 | 0 | [
"region:us"
] | null | 2023-12-11T20:34:01Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype: float64
- name: dataset_name
dtype: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 232895922
num_examples: 949728
- name: validation
num_bytes: 17255970
num_examples: 69711
- name: test
num_bytes: 96102951
num_examples: 425205
download_size: 123150665
dataset_size: 346254843
---
Original Dataset from: https://huggingface.co/datasets/glue
This dataset is adapted from https://huggingface.co/datasets/gmongaras/BERT_Base_Cased_512_GLUE
Every split besides the ax split is in this dataset.
Lines above 512 tokens from the BERT-cased (bert-base-cased) tokenizer are removed in the original dataset
If in any case the sentences are longer than 512 tokens, they are subsetted.
Original labels and dataset categories are retained. | [
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Astris/Nectar-DeDup-Embed | Astris | 2023-12-13T07:08:39Z | 387 | 0 | [
"region:us"
] | null | 2023-12-13T03:48:23Z | ---
dataset_info:
features:
- name: answer
dtype: string
- name: model
dtype: string
- name: rank
dtype: uint16
- name: vector
sequence: float32
splits:
- name: train
num_bytes: 5985643054
num_examples: 1197239
download_size: 2896281236
dataset_size: 5985643054
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Berkeley Nest's Nectar dataset, with prompts removed and duplicate responses (regardless of model) removed. Vector embeddings were generated with BGE-Large-EN-V1.5
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IES-Rafael-Alberti/letras-carnaval-cadiz | IES-Rafael-Alberti | 2023-12-13T19:57:16Z | 363 | 2 | [
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"lyrics",
"carnival",
"cadiz",
"region:us"
] | [] | 2023-04-04T10:34:51Z | ---
annotations_creators:
- no-annotation
language:
- es
language_creators:
- machine-generated
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: letrascarnavalcadiz
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- lyrics
- carnival
- cadiz
task_categories: []
task_ids: []
---
# Dataset Card for Letras Carnaval Cádiz

<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz/blob/main/README_es.md">Español</a>
<p>
</h4>
## Dataset Description
- **Homepage:** https://letrascarnavalcadiz.com
- **Repository:** https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz
- **Point of Contact:** [email protected]
### Changelog
|Release|Description|
|-|-|
|v1.0| Initial release of the dataset. Included more than 1K lyrics. It is necessary to verify the accuracy of the data, especially the subset midaccurate. |
### Dataset Summary
This dataset is a comprehensive collection of lyrics from the Carnaval de Cádiz, a significant cultural heritage of the city of Cádiz, Spain. Despite its cultural importance, there has been a lack of a structured database for these lyrics, hindering research and public access to this cultural heritage. This dataset aims to address this gap.
The dataset was created by the Cádiz AI Learning Community, a branch of the non-profit association Spain AI, and was developed by Iván Romero Reyna and Jesús Federico Franco Medinilla, students of the Specialization Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the 2022-2023 academic year. The project is supervised by Jesús Carlos Avecilla de la Herrán, a computational linguist.
Collaboration is encouraged, with individuals able to verify the different records of the dataset at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com), ensuring the transcription of the lyrics and all data are correct. New lyrics can also be added to the dataset. Corrections and additions are not immediately reflected in the dataset but are updated periodically.
For more information or to report a problem, you can write to [[email protected]](mailto:[email protected]).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in Spanish, reflecting the language of the Carnaval de Cádiz.
## Dataset Structure
### Data Instances
A typical instance in the dataset is formatted in JSON and contains the following fields:
```json
{
"id": "9de8647521b728c45ff45c1c11208708d055397fd7781b31cf91b473dff224d5",
"authors": ["Juan Carlos Aragón Becerra"],
"song_type": 2,
"year": "2018",
"group": "Los Mafiosos",
"group_type": 2,
"lyrics": [
"Mujer va llegando el momento",
"de ser la que lleve la rienda",
"el camino ha sido largo y polvoriento",
"pero ya no habrá varón que te detenga",
"gritad larga vida a la reina",
"que va a comenzar tu gobierno",
"ojalá no heredes nada",
"de aquel macho que te odiaba",
"porque en el fondo sabía",
"que ya tú te le acercabas",
"y el contigo no podía",
"ten en cuenta cuando hagas justicia",
"de volver a nivelar la balanza",
"y aguantar aunque tragando saliva",
"el deseo de venganza",
"de ser oh humano fatal",
"de ser o que puedo entender",
"tan solo con una mirada",
"la llaga que baña tu alma y tu piel",
"que te sirva la experiencia",
"del macho de la manada",
"la fuerza no vale nada",
"si no es con la inteligencia",
"y ojalá que tu conciencia",
"a mí me brinde la suerte",
"de nunca volver a verte",
"con los pies en una iglesia",
"que ella fue quien escribió",
"que ella fue quien escribió",
"la historia contra vosotras",
"y encima se la cobró",
"y encima se la cobró",
"con mil millones de devotas",
"ojalá que tu corona y tu bandera",
"abran paso a una vida nueva",
"como un mundo en primavera",
"ojalá que a ti no te envenene el poder",
"y que no dejes nunca de ser la mujer",
"que siempre fue nuestra gran compañera"
]
}
```
The `id` field uniquely identifies each instance in the dataset, providing a way to reference specific entries. The `authors`, `song_type`, `year`, `group`, and `group_type` fields provide context for the lyrics, while the `lyrics` field itself contains the actual text of the song. The relationships between these fields are implicit in the structure of the dataset, with each instance representing a single song from the Carnaval de Cádiz.
### Data Fields
`id`
Unique identifier for each song in the dataset. A SHA-256 hash calculated from the first four verses of the lyrics and the group name, with all spaces removed and converted to lowercase (string).
`authors`
List of authors who have written the song (string array).
`song_type`
The type of song (1: presentación, 2: pasodoble/tango, 3: cuplé, 4: estribillo, 5: popurrí, 6: cuarteta).
`year`
Year the song was written or performed (string).
`group`
Name of the group that performed the song (string).
`group_type`
The type of the group (1: coro, 2: comparsa, 3: chirigota, 4: cuarteto).
`lyrics`
The lyrics of the song, represented as an array of verses (string array).
### Data Splits
This dataset does not have traditional training, validation, and test splits. Instead, it is divided into two subsets: "accurate" and "midaccurate".
The "accurate" subset contains 958 instances. All fields of first 957 instances in this subset have been obtained through web scraping and have undergone at least one human review for accuracy. The rest have been added by users at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com).
The "midaccurate" subset contains 226 instances. The 'group' and 'lyrics' fields in this subset were collected through web scraping, but the remaining fields were filled in by querying language models connected to the Internet. Therefore, the data in these fields may not be accurate.
| Subset | Instances |
|-------------|----------:|
| Accurate | 958 |
| Midaccurate | 226 |
Please note that the division into subsets is based on the method and reliability of data collection, rather than a random or stratified split typically used in machine learning tasks. Users of the dataset should consider this when deciding how to use the data.
## Dataset Creation
### Curation Rationale
The dataset was created to address a significant need in the cultural heritage of the city of Cádiz, Spain. The Carnaval de Cádiz is a major cultural event, yet there was no structured database of its lyrics that could be consulted for research or public access. This lack of a structured database hindered the exploration and appreciation of this cultural heritage. The dataset was curated to respond to this need.
### Source Data
#### Initial Data Collection and Normalization
The initial collection of lyrics was carried out through automatic scraping of various websites and multimedia content on the Internet. To maximize the number of records with minimal effort, all collection is being done using different Artificial Intelligence models.
#### Who are the source language producers?
The source language producers of the dataset are the authors and performers of the songs from the Carnaval de Cádiz. These include a wide range of individuals and groups who have participated in the Carnaval over the years. The dataset does not include self-reported demographic or identity information for these individuals or groups.
The data in the dataset was collected from two websites: https://www.alsondelcarnaval.es and http://letrasdesdeelparaiso.blogspot.com. The first 957 instances of "accurate" subset of the dataset was collected from the former, while the "midaccurate" subset was collected from the latter. The data was extracted through automatic web scraping, and in the case of the "midaccurate" subset, some fields were filled in by querying language models connected to the Internet.
The rest of "accurate" subset have been added by users at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com).
### Personal and Sensitive Information
The only sensitive information in the dataset is the names and surnames of the authors of the lyrics.
## Considerations for Using the Data
### Social Impact of Dataset
The use of this dataset has significant social impact.
Firstly, this dataset can positively contribute to the understanding and preservation of Cadiz's culture and traditions, as the Carnaval de Cádiz is an integral part of the city's cultural identity. By providing an accessible and easily searchable resource for carnival song lyrics, this dataset can assist cultural researchers, linguists, and the general public in better understanding and appreciating the rich tradition of the Carnaval de Cádiz.
Additionally, this dataset can be utilized to enhance natural language processing (NLP) technologies in Spanish, a language that can sometimes be underrepresented in NLP research. By providing a high-quality, culture-specific Spanish text corpus, this dataset can aid in improving the accuracy and cultural relevance of Spanish NLP models.
However, there are also risks associated with the use of this dataset. For instance, if used to train text generation models, these models could generate content that reinforces cultural stereotypes or perpetuates existing biases. Moreover, the automatic interpretation of carnival song lyrics can be challenging due to cultural and linguistic subtleties, and errors in this interpretation could lead to misunderstandings or misrepresentations of Cadiz's culture.
Finally, although this dataset does not contain a low-resource or underrepresented language, it does focus on a specific cultural tradition from a specific region of Spain. Therefore, its use can impact the Cadiz community by helping to preserve and disseminate its unique culture and traditions.
### Discussion of Biases
The dataset is subject to several biases due to the nature of the data collection and the historical context of the Cadiz Carnival.
Firstly, there is a temporal bias in the dataset. More recent lyrics are overrepresented compared to older ones, as there is more information available on the internet about modern groups. This may lead to a skewed understanding of the evolution of the Carnival's themes over time.
Secondly, the dataset exhibits a popularity bias. Lyrics from more popular groups are overrepresented because individuals have chosen to write about them more frequently. This could potentially limit the diversity of styles and themes represented in the dataset.
Thirdly, there is a competition bias. Lyrics from groups that advanced further in the competition stages are overrepresented, resulting in more available lyrics from these groups. This might lead to an overemphasis on the styles and themes that tend to be more successful in the competition.
Lastly, the dataset reflects a gender bias. Given that there have historically been more male authors than female authors in the Cadiz Carnival, the majority of the dataset consists of lyrics written by men. This could potentially limit the representation of diverse perspectives and themes in the lyrics.
To mitigate these biases, we actively encourage the participation of the community. By verifying the different records of the dataset, reviewing the transcription of the lyrics and all the data for accuracy, and adding new lyrics, we hope to broaden the diversity and representation.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- Iván Romero Reyna. Student of the Specialisation Course in Artificial Intelligence and Big Data at [IES Rafael Alberti](https://iesrafaelalberti.es) during the academic year 2022-2023.
- Jesús Federico Franco Medinilla. Student of the Specialisation Course in Artificial Intelligence and Big Data at [IES Rafael Alberti](https://iesrafaelalberti.es) during the academic year 2022-2023.
- Jesús Carlos Avecilla de la Herrán. Promoter in [Cádiz AI](https://www.spain-ai.com).
### Licensing Information
[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0)
### Citation Information
```
@misc{letrascarnavalcadiz2023,
author = {Romero Reyna, Iván and Franco Medinilla, Jesús Federico and Avecilla de la Herrán, Jesús Carlos},
title = {letras-carnaval-cadiz},
year = {2023},
url = {https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz}
}
```
### Contributions
Thanks to [@ivanro](https://huggingface.co/ivanro), [@jframed281](https://huggingface.co/jframed281) for adding this dataset.
Thanks to all the reviewers and contributors at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com). | [
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open-llm-leaderboard/details_gpt2 | open-llm-leaderboard | 2023-12-16T13:35:31Z | 339 | 0 | [
"region:us"
] | null | 2023-08-18T14:35:26Z | ---
pretty_name: Evaluation run of gpt2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 65 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 19 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_gpt2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T13:32:55.332102](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2023-12-16T13-32-55.332102.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2578870030806924,\n\
\ \"acc_stderr\": 0.030639295152135662,\n \"acc_norm\": 0.2586921012373598,\n\
\ \"acc_norm_stderr\": 0.031410248419889694,\n \"mc1\": 0.22766217870257038,\n\
\ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4073203809998297,\n\
\ \"mc2_stderr\": 0.014931113118872399\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.19880546075085323,\n \"acc_stderr\": 0.011662850198175539,\n\
\ \"acc_norm\": 0.21928327645051193,\n \"acc_norm_stderr\": 0.012091245787615723\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.29267078271260705,\n\
\ \"acc_stderr\": 0.004540586983229993,\n \"acc_norm\": 0.3158733320055766,\n\
\ \"acc_norm_stderr\": 0.004639126951051421\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n\
\ \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n\
\ \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.0301675334686327,\n\
\ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.0301675334686327\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.16,\n\
\ \"acc_stderr\": 0.03684529491774711,\n \"acc_norm\": 0.16,\n \
\ \"acc_norm_stderr\": 0.03684529491774711\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.23773584905660378,\n \"acc_stderr\": 0.02619980880756194,\n\
\ \"acc_norm\": 0.23773584905660378,\n \"acc_norm_stderr\": 0.02619980880756194\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \
\ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\"\
: 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.23699421965317918,\n\
\ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n\
\ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.16,\n\
\ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.2765957446808511,\n \"acc_stderr\": 0.029241883869628834,\n\
\ \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.029241883869628834\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\
\ \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n\
\ \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\
\ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\
acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.14285714285714285,\n\
\ \"acc_stderr\": 0.0312984318574381,\n \"acc_norm\": 0.14285714285714285,\n\
\ \"acc_norm_stderr\": 0.0312984318574381\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263686,\n \
\ \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263686\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.3,\n \"acc_stderr\": 0.026069362295335137,\n \"acc_norm\": 0.3,\n\
\ \"acc_norm_stderr\": 0.026069362295335137\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114475,\n\
\ \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114475\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\"\
: 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.35353535353535354,\n \"acc_stderr\": 0.03406086723547153,\n \"\
acc_norm\": 0.35353535353535354,\n \"acc_norm_stderr\": 0.03406086723547153\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.36787564766839376,\n \"acc_stderr\": 0.03480175668466036,\n\
\ \"acc_norm\": 0.36787564766839376,\n \"acc_norm_stderr\": 0.03480175668466036\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2794871794871795,\n \"acc_stderr\": 0.022752388839776826,\n\
\ \"acc_norm\": 0.2794871794871795,\n \"acc_norm_stderr\": 0.022752388839776826\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073838,\n \
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073838\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136098,\n\
\ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136098\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763744,\n \"\
acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763744\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.3486238532110092,\n \"acc_stderr\": 0.020431254090714328,\n \"\
acc_norm\": 0.3486238532110092,\n \"acc_norm_stderr\": 0.020431254090714328\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.2549019607843137,\n\
\ \"acc_stderr\": 0.030587591351604243,\n \"acc_norm\": 0.2549019607843137,\n\
\ \"acc_norm_stderr\": 0.030587591351604243\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n\
\ \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3004484304932735,\n\
\ \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.3004484304932735,\n\
\ \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.038808483010823944,\n\
\ \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.038808483010823944\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.32231404958677684,\n \"acc_stderr\": 0.04266416363352168,\n \"\
acc_norm\": 0.32231404958677684,\n \"acc_norm_stderr\": 0.04266416363352168\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\
\ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.21296296296296297,\n\
\ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\
\ \"acc_stderr\": 0.04059867246952688,\n \"acc_norm\": 0.24107142857142858,\n\
\ \"acc_norm_stderr\": 0.04059867246952688\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.34951456310679613,\n \"acc_stderr\": 0.04721188506097173,\n\
\ \"acc_norm\": 0.34951456310679613,\n \"acc_norm_stderr\": 0.04721188506097173\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.1752136752136752,\n\
\ \"acc_stderr\": 0.02490443909891822,\n \"acc_norm\": 0.1752136752136752,\n\
\ \"acc_norm_stderr\": 0.02490443909891822\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.21839080459770116,\n\
\ \"acc_stderr\": 0.01477435831993449,\n \"acc_norm\": 0.21839080459770116,\n\
\ \"acc_norm_stderr\": 0.01477435831993449\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.02289408248992599,\n\
\ \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.02289408248992599\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\
\ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\
\ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.023805186524888146,\n\
\ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.023805186524888146\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24758842443729903,\n\
\ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.24758842443729903,\n\
\ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.22530864197530864,\n \"acc_stderr\": 0.023246202647819746,\n\
\ \"acc_norm\": 0.22530864197530864,\n \"acc_norm_stderr\": 0.023246202647819746\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880592,\n \
\ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880592\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24771838331160365,\n\
\ \"acc_stderr\": 0.011025499291443737,\n \"acc_norm\": 0.24771838331160365,\n\
\ \"acc_norm_stderr\": 0.011025499291443737\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.030161911930767102,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.030161911930767102\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2647058823529412,\n \"acc_stderr\": 0.017848089574913222,\n \
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.017848089574913222\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\
\ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\
\ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n \
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n\
\ \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n\
\ \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.1927710843373494,\n\
\ \"acc_stderr\": 0.030709824050565274,\n \"acc_norm\": 0.1927710843373494,\n\
\ \"acc_norm_stderr\": 0.030709824050565274\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\
\ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22766217870257038,\n\
\ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4073203809998297,\n\
\ \"mc2_stderr\": 0.014931113118872399\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.014051745961790513\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \
\ \"acc_stderr\": 0.0022675371022544736\n }\n}\n```"
repo_url: https://huggingface.co/gpt2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|arc:challenge|25_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|arc:challenge|25_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_drop_0
data_files:
- split: 2023_09_14T13_54_21.687636
path:
- '**/details_harness|drop|0_2023-09-14T13-54-21.687636.parquet'
- split: 2023_09_15T12_28_23.937147
path:
- '**/details_harness|drop|0_2023-09-15T12-28-23.937147.parquet'
- split: 2023_09_15T12_47_31.231445
path:
- '**/details_harness|drop|0_2023-09-15T12-47-31.231445.parquet'
- split: latest
path:
- '**/details_harness|drop|0_2023-09-15T12-47-31.231445.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|drop|3_2023-11-21T18-07-07.067275.parquet'
- split: 2023_11_29T12_47_35.686694
path:
- '**/details_harness|drop|3_2023-11-29T12-47-35.686694.parquet'
- split: 2023_11_29T12_58_42.860611
path:
- '**/details_harness|drop|3_2023-11-29T12-58-42.860611.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-29T12-58-42.860611.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|gsm8k|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_11_29T12_47_35.686694
path:
- '**/details_harness|gsm8k|5_2023-11-29T12-47-35.686694.parquet'
- split: 2023_11_29T12_58_42.860611
path:
- '**/details_harness|gsm8k|5_2023-11-29T12-58-42.860611.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|gsm8k|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hellaswag|10_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hellaswag|10_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-21T18-07-07.067275.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet'
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-21T18-07-07.067275.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T13-32-55.332102.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_06T15_19_52.414673
path:
- '**/details_harness|winogrande|5_2023-09-06T15-19-52.414673.parquet'
- split: 2023_09_06T15_22_24.734466
path:
- '**/details_harness|winogrande|5_2023-09-06T15-22-24.734466.parquet'
- split: 2023_09_06T15_24_04.768979
path:
- '**/details_harness|winogrande|5_2023-09-06T15-24-04.768979.parquet'
- split: 2023_09_07T12_01_51.839651
path:
- '**/details_harness|winogrande|5_2023-09-07T12-01-51.839651.parquet'
- split: 2023_09_07T12_04_01.189528
path:
- '**/details_harness|winogrande|5_2023-09-07T12-04-01.189528.parquet'
- split: 2023_09_07T12_08_17.821371
path:
- '**/details_harness|winogrande|5_2023-09-07T12-08-17.821371.parquet'
- split: 2023_09_07T12_10_30.286469
path:
- '**/details_harness|winogrande|5_2023-09-07T12-10-30.286469.parquet'
- split: 2023_11_21T18_07_07.067275
path:
- '**/details_harness|winogrande|5_2023-11-21T18-07-07.067275.parquet'
- split: 2023_11_29T12_47_35.686694
path:
- '**/details_harness|winogrande|5_2023-11-29T12-47-35.686694.parquet'
- split: 2023_11_29T12_58_42.860611
path:
- '**/details_harness|winogrande|5_2023-11-29T12-58-42.860611.parquet'
- split: 2023_12_16T13_32_55.332102
path:
- '**/details_harness|winogrande|5_2023-12-16T13-32-55.332102.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T13-32-55.332102.parquet'
- config_name: results
data_files:
- split: 2023_09_06T12_19_07.283399
path:
- results_2023-09-06T12-19-07.283399.parquet
- split: 2023_09_06T12_21_24.071294
path:
- results_2023-09-06T12-21-24.071294.parquet
- split: 2023_09_06T12_24_13.323279
path:
- results_2023-09-06T12-24-13.323279.parquet
- split: 2023_09_06T13_26_17.619860
path:
- results_2023-09-06T13-26-17.619860.parquet
- split: 2023_09_06T15_15_44.379880
path:
- results_2023-09-06T15-15-44.379880.parquet
- split: 2023_09_06T15_19_52.414673
path:
- results_2023-09-06T15-19-52.414673.parquet
- split: 2023_09_06T15_22_24.734466
path:
- results_2023-09-06T15-22-24.734466.parquet
- split: 2023_09_06T15_24_04.768979
path:
- results_2023-09-06T15-24-04.768979.parquet
- split: 2023_09_07T12_01_51.839651
path:
- results_2023-09-07T12-01-51.839651.parquet
- split: 2023_09_07T12_04_01.189528
path:
- results_2023-09-07T12-04-01.189528.parquet
- split: 2023_09_07T12_08_17.821371
path:
- results_2023-09-07T12-08-17.821371.parquet
- split: 2023_09_07T12_10_30.286469
path:
- results_2023-09-07T12-10-30.286469.parquet
- split: 2023_09_14T13_54_21.687636
path:
- results_2023-09-14T13-54-21.687636.parquet
- split: 2023_09_15T12_28_23.937147
path:
- results_2023-09-15T12-28-23.937147.parquet
- split: 2023_09_15T12_47_31.231445
path:
- results_2023-09-15T12-47-31.231445.parquet
- split: 2023_11_21T18_07_07.067275
path:
- results_2023-11-21T18-07-07.067275.parquet
- split: 2023_11_29T12_47_35.686694
path:
- results_2023-11-29T12-47-35.686694.parquet
- split: 2023_11_29T12_58_42.860611
path:
- results_2023-11-29T12-58-42.860611.parquet
- split: 2023_12_16T13_32_55.332102
path:
- results_2023-12-16T13-32-55.332102.parquet
- split: latest
path:
- results_2023-12-16T13-32-55.332102.parquet
---
# Dataset Card for Evaluation run of gpt2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 65 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 19 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_gpt2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T13:32:55.332102](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2023-12-16T13-32-55.332102.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.2578870030806924,
"acc_stderr": 0.030639295152135662,
"acc_norm": 0.2586921012373598,
"acc_norm_stderr": 0.031410248419889694,
"mc1": 0.22766217870257038,
"mc1_stderr": 0.01467925503211107,
"mc2": 0.4073203809998297,
"mc2_stderr": 0.014931113118872399
},
"harness|arc:challenge|25": {
"acc": 0.19880546075085323,
"acc_stderr": 0.011662850198175539,
"acc_norm": 0.21928327645051193,
"acc_norm_stderr": 0.012091245787615723
},
"harness|hellaswag|10": {
"acc": 0.29267078271260705,
"acc_stderr": 0.004540586983229993,
"acc_norm": 0.3158733320055766,
"acc_norm_stderr": 0.004639126951051421
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.23703703703703705,
"acc_stderr": 0.03673731683969506,
"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.03673731683969506
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.16447368421052633,
"acc_stderr": 0.0301675334686327,
"acc_norm": 0.16447368421052633,
"acc_norm_stderr": 0.0301675334686327
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.16,
"acc_stderr": 0.03684529491774711,
"acc_norm": 0.16,
"acc_norm_stderr": 0.03684529491774711
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.23773584905660378,
"acc_stderr": 0.02619980880756194,
"acc_norm": 0.23773584905660378,
"acc_norm_stderr": 0.02619980880756194
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.19,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.19,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.23699421965317918,
"acc_stderr": 0.03242414757483098,
"acc_norm": 0.23699421965317918,
"acc_norm_stderr": 0.03242414757483098
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.043364327079931785,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.043364327079931785
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.16,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.16,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2765957446808511,
"acc_stderr": 0.029241883869628834,
"acc_norm": 0.2765957446808511,
"acc_norm_stderr": 0.029241883869628834
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.042270544512322,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.042270544512322
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.25396825396825395,
"acc_stderr": 0.022418042891113942,
"acc_norm": 0.25396825396825395,
"acc_norm_stderr": 0.022418042891113942
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.14285714285714285,
"acc_stderr": 0.0312984318574381,
"acc_norm": 0.14285714285714285,
"acc_norm_stderr": 0.0312984318574381
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.15,
"acc_stderr": 0.035887028128263686,
"acc_norm": 0.15,
"acc_norm_stderr": 0.035887028128263686
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.3,
"acc_stderr": 0.026069362295335137,
"acc_norm": 0.3,
"acc_norm_stderr": 0.026069362295335137
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.26108374384236455,
"acc_stderr": 0.030903796952114475,
"acc_norm": 0.26108374384236455,
"acc_norm_stderr": 0.030903796952114475
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.35353535353535354,
"acc_stderr": 0.03406086723547153,
"acc_norm": 0.35353535353535354,
"acc_norm_stderr": 0.03406086723547153
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.36787564766839376,
"acc_stderr": 0.03480175668466036,
"acc_norm": 0.36787564766839376,
"acc_norm_stderr": 0.03480175668466036
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2794871794871795,
"acc_stderr": 0.022752388839776826,
"acc_norm": 0.2794871794871795,
"acc_norm_stderr": 0.022752388839776826
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26666666666666666,
"acc_stderr": 0.026962424325073838,
"acc_norm": 0.26666666666666666,
"acc_norm_stderr": 0.026962424325073838
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.28991596638655465,
"acc_stderr": 0.029472485833136098,
"acc_norm": 0.28991596638655465,
"acc_norm_stderr": 0.029472485833136098
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2781456953642384,
"acc_stderr": 0.03658603262763744,
"acc_norm": 0.2781456953642384,
"acc_norm_stderr": 0.03658603262763744
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.3486238532110092,
"acc_stderr": 0.020431254090714328,
"acc_norm": 0.3486238532110092,
"acc_norm_stderr": 0.020431254090714328
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.030587591351604243,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.030587591351604243
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.2489451476793249,
"acc_stderr": 0.028146970599422644,
"acc_norm": 0.2489451476793249,
"acc_norm_stderr": 0.028146970599422644
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.3004484304932735,
"acc_stderr": 0.030769352008229143,
"acc_norm": 0.3004484304932735,
"acc_norm_stderr": 0.030769352008229143
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.26717557251908397,
"acc_stderr": 0.038808483010823944,
"acc_norm": 0.26717557251908397,
"acc_norm_stderr": 0.038808483010823944
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.32231404958677684,
"acc_stderr": 0.04266416363352168,
"acc_norm": 0.32231404958677684,
"acc_norm_stderr": 0.04266416363352168
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.21296296296296297,
"acc_stderr": 0.03957835471980981,
"acc_norm": 0.21296296296296297,
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```
## Dataset Details
### Dataset Description
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[More Information Needed] | [
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hezarai/persian-license-plate-v1 | hezarai | 2023-12-15T13:08:45Z | 331 | 0 | [
"task_categories:image-to-text",
"language:fa",
"region:us"
] | [
"image-to-text"
] | 2023-10-14T12:56:01Z | ---
task_categories:
- image-to-text
language:
- fa
pretty_name: PersianLicensePlate
---
> Dataset is downloaded from [here](https://ceit.aut.ac.ir/~keyvanrad/download/ML971/project/) which was provided at Amirkabir University of Technology.
> The datas then labeled by the authors.
> Experimental results show that the fine-tuned model works well in Persian License Plate.
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nuprl/CanItEdit | nuprl | 2023-12-14T20:57:48Z | 326 | 0 | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:mit",
"code-generation",
"region:us"
] | [
"text2text-generation"
] | 2023-12-11T14:13:35Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: CanItEdit
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
tags:
- code-generation
paperswithcode_id: canitedit
dataset_info:
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: full_name
dtype: string
- name: before
dtype: string
- name: after
dtype: string
- name: tests
dtype: string
- name: instruction_descriptive
dtype: string
- name: instruction_humane
dtype: string
- name: taxonomy
struct:
- name: change_kind
dtype: string
- name: libraries
sequence: string
- name: topic
dtype: string
splits:
- name: test
num_bytes: 301982
num_examples: 54
download_size: 136181
dataset_size: 301982
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
CanItEdit is a benchmark for evaluating LLMs on instructional code editing, the task of updating a program given a natural language instruction. The benchmark contains 54 hand-crafted Python programs with before and after code blocks, two types of natural language instructions (descriptive and lazy), and a hidden test suite.
The dataset’s dual natural language instructions test model efficiency in two scenarios:
1) Descriptive: Detailed instructions replicate situations where users provide specific specifications or
another model outlines a plan, similar to Reflexion prompting,
2) Lazy: Informal instructions resemble typical user queries
for LLMs in code generation.
For more information and results see [our paper](https://federico.codes/assets/papers/canitedit.pdf).
## How To Evaluate
All the code for evaluating the benchmark can be found in our [GitHub repository](https://github.com/nuprl/CanItEdit).
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autumnjohnson/ceti_audio | autumnjohnson | 2023-12-12T06:06:50Z | 323 | 0 | [
"size_categories:1K<n<10K",
"region:us"
] | null | 2023-11-06T10:15:17Z | ---
size_categories:
- 1K<n<10K
pretty_name: Project CETI (Cetacean Translation Initiative) audio
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
decode: false
- name: coda_type
dtype: string
- name: path
dtype: string
- name: sampling_rate
dtype: int64
splits:
- name: train
num_bytes: 229777268.74202734
num_examples: 2458
- name: test
num_bytes: 98529390.25797267
num_examples: 1054
download_size: 162197285
dataset_size: 328306659.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "ceti_audio"
## Table of Contents
- [Dataset Card for "ceti\_audio"](#dataset-card-for-ceti_audio)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@autumnjohnson](https://github.com/<github-username>) for adding this dataset. | [
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open-llm-leaderboard/details_gpt2-xl | open-llm-leaderboard | 2023-12-16T14:31:36Z | 315 | 0 | [
"region:us"
] | null | 2023-09-02T12:01:52Z | ---
pretty_name: Evaluation run of gpt2-xl
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [gpt2-xl](https://huggingface.co/gpt2-xl) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_gpt2-xl\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T14:28:59.235900](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2-xl/blob/main/results_2023-12-16T14-28-59.235900.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.26849437510168506,\n\
\ \"acc_stderr\": 0.031366498421091876,\n \"acc_norm\": 0.2702003428835176,\n\
\ \"acc_norm_stderr\": 0.03215444575128815,\n \"mc1\": 0.22031823745410037,\n\
\ \"mc1_stderr\": 0.0145090451714873,\n \"mc2\": 0.38536763571053717,\n\
\ \"mc2_stderr\": 0.014057464412774041\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.2593856655290102,\n \"acc_stderr\": 0.012808273573927104,\n\
\ \"acc_norm\": 0.302901023890785,\n \"acc_norm_stderr\": 0.013428241573185347\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3981278629755029,\n\
\ \"acc_stderr\": 0.004885116465550274,\n \"acc_norm\": 0.5136427006572396,\n\
\ \"acc_norm_stderr\": 0.004987923636628551\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2074074074074074,\n\
\ \"acc_stderr\": 0.03502553170678318,\n \"acc_norm\": 0.2074074074074074,\n\
\ \"acc_norm_stderr\": 0.03502553170678318\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.18421052631578946,\n \"acc_stderr\": 0.0315469804508223,\n\
\ \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.28,\n\
\ \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.29056603773584905,\n \"acc_stderr\": 0.027943219989337145,\n\
\ \"acc_norm\": 0.29056603773584905,\n \"acc_norm_stderr\": 0.027943219989337145\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2847222222222222,\n\
\ \"acc_stderr\": 0.03773809990686935,\n \"acc_norm\": 0.2847222222222222,\n\
\ \"acc_norm_stderr\": 0.03773809990686935\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.30057803468208094,\n\
\ \"acc_stderr\": 0.03496101481191181,\n \"acc_norm\": 0.30057803468208094,\n\
\ \"acc_norm_stderr\": 0.03496101481191181\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179962,\n\
\ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179962\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n\
\ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.2978723404255319,\n \"acc_stderr\": 0.029896145682095455,\n\
\ \"acc_norm\": 0.2978723404255319,\n \"acc_norm_stderr\": 0.029896145682095455\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n\
\ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.23809523809523808,\n \"acc_stderr\": 0.021935878081184756,\n \"\
acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.021935878081184756\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n\
\ \"acc_stderr\": 0.04240799327574925,\n \"acc_norm\": 0.3412698412698413,\n\
\ \"acc_norm_stderr\": 0.04240799327574925\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24838709677419354,\n\
\ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.24838709677419354,\n\
\ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.21674876847290642,\n \"acc_stderr\": 0.028990331252516235,\n\
\ \"acc_norm\": 0.21674876847290642,\n \"acc_norm_stderr\": 0.028990331252516235\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \"acc_norm\"\
: 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.3090909090909091,\n \"acc_stderr\": 0.036085410115739666,\n\
\ \"acc_norm\": 0.3090909090909091,\n \"acc_norm_stderr\": 0.036085410115739666\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.2777777777777778,\n \"acc_stderr\": 0.03191178226713548,\n \"\
acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.03191178226713548\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.21243523316062177,\n \"acc_stderr\": 0.029519282616817258,\n\
\ \"acc_norm\": 0.21243523316062177,\n \"acc_norm_stderr\": 0.029519282616817258\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.34102564102564104,\n \"acc_stderr\": 0.02403548967633507,\n\
\ \"acc_norm\": 0.34102564102564104,\n \"acc_norm_stderr\": 0.02403548967633507\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2518518518518518,\n \"acc_stderr\": 0.02646611753895991,\n \
\ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.02646611753895991\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868956,\n\
\ \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868956\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.26490066225165565,\n \"acc_stderr\": 0.03603038545360384,\n \"\
acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.03603038545360384\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.3321100917431193,\n \"acc_stderr\": 0.020192682985423347,\n \"\
acc_norm\": 0.3321100917431193,\n \"acc_norm_stderr\": 0.020192682985423347\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.39814814814814814,\n \"acc_stderr\": 0.033384734032074016,\n \"\
acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.033384734032074016\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.22549019607843138,\n \"acc_stderr\": 0.029331162294251714,\n \"\
acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.029331162294251714\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n \
\ \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.19282511210762332,\n\
\ \"acc_stderr\": 0.026478240960489365,\n \"acc_norm\": 0.19282511210762332,\n\
\ \"acc_norm_stderr\": 0.026478240960489365\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.03880848301082396,\n\
\ \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.03880848301082396\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2727272727272727,\n \"acc_stderr\": 0.04065578140908705,\n \"\
acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.04065578140908705\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n\
\ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\
\ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.2392638036809816,\n \"acc_stderr\": 0.03351953879521269,\n\
\ \"acc_norm\": 0.2392638036809816,\n \"acc_norm_stderr\": 0.03351953879521269\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\
\ \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n\
\ \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.2621359223300971,\n \"acc_stderr\": 0.04354631077260597,\n\
\ \"acc_norm\": 0.2621359223300971,\n \"acc_norm_stderr\": 0.04354631077260597\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.27350427350427353,\n\
\ \"acc_stderr\": 0.029202540153431177,\n \"acc_norm\": 0.27350427350427353,\n\
\ \"acc_norm_stderr\": 0.029202540153431177\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2681992337164751,\n\
\ \"acc_stderr\": 0.015842430835269445,\n \"acc_norm\": 0.2681992337164751,\n\
\ \"acc_norm_stderr\": 0.015842430835269445\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2398843930635838,\n \"acc_stderr\": 0.02298959254312357,\n\
\ \"acc_norm\": 0.2398843930635838,\n \"acc_norm_stderr\": 0.02298959254312357\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\
\ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\
\ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.28104575163398693,\n \"acc_stderr\": 0.02573885479781873,\n\
\ \"acc_norm\": 0.28104575163398693,\n \"acc_norm_stderr\": 0.02573885479781873\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.21221864951768488,\n\
\ \"acc_stderr\": 0.023222756797435122,\n \"acc_norm\": 0.21221864951768488,\n\
\ \"acc_norm_stderr\": 0.023222756797435122\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.24691358024691357,\n \"acc_stderr\": 0.02399350170904211,\n\
\ \"acc_norm\": 0.24691358024691357,\n \"acc_norm_stderr\": 0.02399350170904211\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432407,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432407\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.25945241199478486,\n\
\ \"acc_stderr\": 0.011195262076350316,\n \"acc_norm\": 0.25945241199478486,\n\
\ \"acc_norm_stderr\": 0.011195262076350316\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.20955882352941177,\n \"acc_stderr\": 0.02472311040767705,\n\
\ \"acc_norm\": 0.20955882352941177,\n \"acc_norm_stderr\": 0.02472311040767705\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.238562091503268,\n \"acc_stderr\": 0.017242385828779586,\n \
\ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.017242385828779586\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3181818181818182,\n\
\ \"acc_stderr\": 0.04461272175910508,\n \"acc_norm\": 0.3181818181818182,\n\
\ \"acc_norm_stderr\": 0.04461272175910508\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2897959183673469,\n \"acc_stderr\": 0.02904308868330435,\n\
\ \"acc_norm\": 0.2897959183673469,\n \"acc_norm_stderr\": 0.02904308868330435\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n\
\ \"acc_stderr\": 0.030769444967296024,\n \"acc_norm\": 0.2537313432835821,\n\
\ \"acc_norm_stderr\": 0.030769444967296024\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3373493975903614,\n\
\ \"acc_stderr\": 0.03680783690727581,\n \"acc_norm\": 0.3373493975903614,\n\
\ \"acc_norm_stderr\": 0.03680783690727581\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.32748538011695905,\n \"acc_stderr\": 0.03599335771456027,\n\
\ \"acc_norm\": 0.32748538011695905,\n \"acc_norm_stderr\": 0.03599335771456027\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22031823745410037,\n\
\ \"mc1_stderr\": 0.0145090451714873,\n \"mc2\": 0.38536763571053717,\n\
\ \"mc2_stderr\": 0.014057464412774041\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5824782951854776,\n \"acc_stderr\": 0.013859978264440248\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01288855193328279,\n \
\ \"acc_stderr\": 0.0031069012664996505\n }\n}\n```"
repo_url: https://huggingface.co/gpt2-xl
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|arc:challenge|25_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|arc:challenge|25_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_06T15_15_36.516909
path:
- '**/details_harness|gsm8k|5_2023-12-06T15-15-36.516909.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|gsm8k|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hellaswag|10_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hellaswag|10_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T12:01:37.784135.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-02T12:01:37.784135.parquet'
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T14-28-59.235900.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_16T14_28_59.235900
path:
- '**/details_harness|winogrande|5_2023-12-16T14-28-59.235900.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T14-28-59.235900.parquet'
- config_name: results
data_files:
- split: 2023_09_02T12_01_37.784135
path:
- results_2023-09-02T12:01:37.784135.parquet
- split: 2023_12_06T15_15_36.516909
path:
- results_2023-12-06T15-15-36.516909.parquet
- split: 2023_12_16T14_28_59.235900
path:
- results_2023-12-16T14-28-59.235900.parquet
- split: latest
path:
- results_2023-12-16T14-28-59.235900.parquet
---
# Dataset Card for Evaluation run of gpt2-xl
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [gpt2-xl](https://huggingface.co/gpt2-xl) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_gpt2-xl",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T14:28:59.235900](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2-xl/blob/main/results_2023-12-16T14-28-59.235900.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.26849437510168506,
"acc_stderr": 0.031366498421091876,
"acc_norm": 0.2702003428835176,
"acc_norm_stderr": 0.03215444575128815,
"mc1": 0.22031823745410037,
"mc1_stderr": 0.0145090451714873,
"mc2": 0.38536763571053717,
"mc2_stderr": 0.014057464412774041
},
"harness|arc:challenge|25": {
"acc": 0.2593856655290102,
"acc_stderr": 0.012808273573927104,
"acc_norm": 0.302901023890785,
"acc_norm_stderr": 0.013428241573185347
},
"harness|hellaswag|10": {
"acc": 0.3981278629755029,
"acc_stderr": 0.004885116465550274,
"acc_norm": 0.5136427006572396,
"acc_norm_stderr": 0.004987923636628551
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768081,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768081
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2074074074074074,
"acc_stderr": 0.03502553170678318,
"acc_norm": 0.2074074074074074,
"acc_norm_stderr": 0.03502553170678318
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.18421052631578946,
"acc_stderr": 0.0315469804508223,
"acc_norm": 0.18421052631578946,
"acc_norm_stderr": 0.0315469804508223
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.29056603773584905,
"acc_stderr": 0.027943219989337145,
"acc_norm": 0.29056603773584905,
"acc_norm_stderr": 0.027943219989337145
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2847222222222222,
"acc_stderr": 0.03773809990686935,
"acc_norm": 0.2847222222222222,
"acc_norm_stderr": 0.03773809990686935
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.30057803468208094,
"acc_stderr": 0.03496101481191181,
"acc_norm": 0.30057803468208094,
"acc_norm_stderr": 0.03496101481191181
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.19607843137254902,
"acc_stderr": 0.03950581861179962,
"acc_norm": 0.19607843137254902,
"acc_norm_stderr": 0.03950581861179962
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2978723404255319,
"acc_stderr": 0.029896145682095455,
"acc_norm": 0.2978723404255319,
"acc_norm_stderr": 0.029896145682095455
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813344,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813344
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2689655172413793,
"acc_stderr": 0.03695183311650232,
"acc_norm": 0.2689655172413793,
"acc_norm_stderr": 0.03695183311650232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.23809523809523808,
"acc_stderr": 0.021935878081184756,
"acc_norm": 0.23809523809523808,
"acc_norm_stderr": 0.021935878081184756
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3412698412698413,
"acc_stderr": 0.04240799327574925,
"acc_norm": 0.3412698412698413,
"acc_norm_stderr": 0.04240799327574925
},
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```
## Dataset Details
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[More Information Needed] | [
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aeromaki/WOS46985 | aeromaki | 2023-12-14T00:38:13Z | 310 | 0 | [
"region:us"
] | null | 2023-12-14T00:37:55Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: label
sequence: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 59938702.67012876
num_examples: 42286
- name: test
num_bytes: 3331030.394806853
num_examples: 2350
- name: validation
num_bytes: 3329612.9350643824
num_examples: 2349
download_size: 37716140
dataset_size: 66599346.0
---
# Dataset Card for "WOS46985"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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martinsinnona/visdecode | martinsinnona | 2023-12-11T16:31:24Z | 238 | 0 | [
"region:us"
] | null | 2023-06-30T14:39:33Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 964808.0
num_examples: 100
- name: test
num_bytes: 5194.0
num_examples: 1
download_size: 851229
dataset_size: 970002.0
---
# Dataset Card for "ploty"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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trl-lib/ultrachat_200k_chatml | trl-lib | 2023-12-12T11:10:50Z | 220 | 0 | [
"region:us"
] | null | 2023-12-12T11:10:22Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1224177384
num_examples: 207865
- name: test
num_bytes: 135316994
num_examples: 23110
download_size: 676202243
dataset_size: 1359494378
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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pinkieseb/auslegalcorpus | pinkieseb | 2023-12-11T04:01:51Z | 204 | 0 | [
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"legal",
"region:us"
] | null | 2023-12-08T22:24:58Z | ---
license: apache-2.0
language:
- en
tags:
- legal
size_categories:
- 1M<n<10M
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humane-lab/K-HATERS | humane-lab | 2023-12-14T09:25:04Z | 182 | 0 | [
"language:ko",
"license:cc-by-4.0",
"hate speech detection",
"arxiv:2310.15439",
"region:us"
] | null | 2023-10-16T04:29:58Z | ---
license: cc-by-4.0
language:
- ko
pretty_name: K-Haters
tags:
- hate speech detection
---
<!--
# ℹ️ Dataset card for K-HATERS
### Dataset summary
We introduces **K-HATERS**, a new corpus for hate speech detection in Korean, comprising approximately 192K news comments with target-specific offensiveness ratings.
The corpus consists of 192,158 news comments consisting of 184,117 news comments collected by ourselves and 8,041 comments collected from a [previous study](https://aclanthology.org/2020.socialnlp-1.4/).
We collected news comments published through the politics, society and world news sections in Naver News over two months in 2021.
All comments were annotated through CashMission, a crowdsourcing service run by SELECTSTAR.
</br>For more information, please refer to the paper [K-HATERS](https://arxiv.org/abs/2310.15439) published at EMNLP 2023 Findings.
### Supported tasks
- Hate speech detection
- Multi class classification (labels: normal, offensive, L1_hate, L2_hate)
- Binary classifiction (labels: normal, toxic(offensive, L1_hate, L2_hate))
- Rationale prediction (offensiveness, target rationale)
### Data describtion
```
data['train'][42]
{'text': '군대도 안간 놈 이 주둥아리 는 씽씽하네..보수 놈 들..군대는 안가고 애국이냐..#@이름#,#@이름#,',
'label': 'L1_hate',
'target_label': ['political'],
'offensiveness_rationale': [[7, 8], [11, 15], [27, 28]],
'target_rationale': [[24, 26], [46, 51], [52, 57]]}
```
- Abusive language categories (**label**)
- L2_hate: Comments with explicit forms of hate expressions toward one of the groups of protected attributes (e.g., gender, age, race, ...)
- L1_hate: Comments with more implicit forms of hate expressions
- Offensive: Comments that express offensiveness but not toward a protected attribute group
- Normal: The rest comments
- Multi-label target categories (**target_label**): list of offensiveness targets. A comment can have zero or multiple targets.
- List of target categories: gender, age, race, religion, politics, job, disability, individuals, and others.
- Annotators' rationales for the strength of ratings (**offensiveness_rationale**): lists providing annotators' rationales for the strength of ratings. The list includes the start and end indices of highlight spans.
- Annotators' rationales for the target of offensiveness (**target_rationale**)
### Dataset split
We provide the dataset in the form of splits as 172,158 (for train), 10,000 (for validation), and 10,000 (for test). Label ratio was preseved (stratified split).
### Labeling guidelines
Labeling guidelines are available as a part of SELECTSTAR open datasets (in Korean). [link](https://open.selectstar.ai/ko/?page_id=5948)
</br>
# 📜 Data statement
We present the data statement for responsible usage [(Bender and Friedman, 2018)](https://aclanthology.org/Q18-1041/).
### Curation Rationale
We collected the raw data from the news aggregator of Naver, the largest news portal in Korea. We targeted news articles published in the society, world news, and politics sections because discussions are active in the hard news.
### Language Variety
Our dataset consists of the news comments in Korean (ko-KR).
### Speaker Demographic
The user demographic is not available. However, considering that the portal site has the largest share of Korean, it can be assumed that speakers are mostly Korean.
### Annotator Demographic
A total of 405 workers participated in an annotation. 21 workers are 10s, 222 workers are 20s, 116 workers are 30s, 35 workers are 40s, 9 workers are 50s, and 2 workers are 60s.
### Speech Situation
News article in the hard news section deals with controversial events, so there are more likely to exist hate comments or toxicity comments. The target articles were published between July 2021 and August 2021. During that period, the most controversial events were the South Korean presidential election, the Tokyo Olympics, COVID-19, and the Restoration of Taliban Control, etc.
### Text Characteristics
It includes hatred words limited to Korea, such as hatred of certain political orientations and certain groups. For example, '대깨문' (a word that hates former Korean president Moon's supporter), and '꼴페미' (a word that hates feminists)
</br>
# 🤝 License & Contributors
### Licensing information
This dataset is shared under CC-BY 4.0.
</br>According to this license, you are free to use the dataset as long as you provide appropriate attribution (e.g., citing our paper).
### Citation information
```
@article{park2023haters,
title={K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings},
author={Park, Chaewon and Kim, Suhwan and Park, Kyubyong and Park, Kunwoo},
journal={Findings of the EMNLP 2023},
year={2023}
}
```
### Contributions
- Chaewon Park
- Suhwan Kim (TUNiB)
- Kyubyong Park (TUNiB)
- Kunwoo Park
#--> | [
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] |
unalignment/toxic-dpo-v0.1 | unalignment | 2023-12-13T12:08:23Z | 178 | 44 | [
"license:apache-2.0",
"not-for-all-audiences",
"region:us"
] | null | 2023-12-11T15:51:16Z | ---
license: apache-2.0
tags:
- not-for-all-audiences
---
## Toxic-DPO
This is a highly toxic, "harmful" dataset meant to illustrate how DPO can be used to de-censor/unalign a model quite easily using direct-preference-optimization (DPO) using very few examples.
Most of the examples still contain some amount of warnings/disclaimers, so it's still somewhat editorialized.
## Usage restriction
To use this data, you must acknowledge/agree to the following:
- data contained within is "toxic"/"harmful", and contains profanity and other types of sensitive content
- none of the content or views contained in the dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs automatically (llama-2-70b via prompt engineering for chosen and llama-2-13b-chat-hf for rejected)
- you are able to use the dataset lawfully, particularly in locations with less-than-free speech laws
- you, and you alone are responsible for having downloaded and used the dataset, and I am completely indemnified from any and all liabilities
This dataset is meant __*exclusively*__ for academic/research or other non-nefarious use-cases. | [
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argilla/end2end_textclassification_with_suggestions_and_responses | argilla | 2023-12-15T16:54:52Z | 174 | 0 | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-11-29T15:15:26Z | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for end2end_textclassification_with_suggestions_and_responses
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/end2end_textclassification_with_suggestions_and_responses")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/end2end_textclassification_with_suggestions_and_responses")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | FieldTypes.text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | Label | QuestionTypes.label_selection | True | N/A | ['World', 'Sports', 'Business', 'Sci/Tech'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": "record-0",
"fields": {
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": null,
"values": {
"label": {
"value": "Business"
}
}
}
],
"suggestions": [
{
"agent": "cointegrated/rubert-tiny-bilingual-nli",
"question_name": "label",
"score": null,
"type": null,
"value": "Sports"
}
],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": "record-0",
"label": [
{
"status": "submitted",
"user_id": null,
"value": "Business"
}
],
"label-suggestion": "Sports",
"label-suggestion-metadata": {
"agent": "cointegrated/rubert-tiny-bilingual-nli",
"score": null,
"type": null
},
"metadata": "{}",
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `FieldTypes.text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `QuestionTypes.label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `QuestionTypes.label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
Classify the articles into one of the four categories.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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amazingvince/RedPajama-Data-V2-Sample-snapshot-2023-14 | amazingvince | 2023-12-16T14:41:05Z | 169 | 1 | [
"region:us"
] | null | 2023-11-19T22:20:17Z | ---
dataset_info:
features:
- name: raw_content
dtype: string
- name: doc_id
dtype: string
- name: meta
dtype: string
- name: quality_signals
dtype: string
splits:
- name: train
num_bytes: 2595581904
num_examples: 232056
download_size: 1178806632
dataset_size: 2595581904
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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cis-lmu/Glot500 | cis-lmu | 2023-12-15T06:41:30Z | 166 | 5 | [
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"license:other",
"arxiv:2305.12182",
"region:us"
] | null | 2023-11-01T10:25:59Z | ---
license: other
license_name: license
license_link: LICENSE
configs:
- config_name: mlt_Mlym
data_files:
- split: train
path: "mlt_Mlym/train/*.arrow"
- split: dev
path: "mlt_Mlym/dev/*.arrow"
- split: test
path: "mlt_Mlym/test/*.arrow"
- config_name: knv_Latn
data_files:
- split: train
path: "knv_Latn/train/*.arrow"
- split: dev
path: "knv_Latn/dev/*.arrow"
- split: test
path: "knv_Latn/test/*.arrow"
- config_name: guj_Arab
data_files:
- split: train
path: "guj_Arab/train/*.arrow"
- split: dev
path: "guj_Arab/dev/*.arrow"
- split: test
path: "guj_Arab/test/*.arrow"
- config_name: ton_Latn
data_files:
- split: train
path: "ton_Latn/train/*.arrow"
- split: dev
path: "ton_Latn/dev/*.arrow"
- split: test
path: "ton_Latn/test/*.arrow"
- config_name: nld_Latn
data_files:
- split: train
path: "nld_Latn/train/*.arrow"
- split: dev
path: "nld_Latn/dev/*.arrow"
- split: test
path: "nld_Latn/test/*.arrow"
- config_name: tzo_Latn
data_files:
- split: train
path: "tzo_Latn/train/*.arrow"
- split: dev
path: "tzo_Latn/dev/*.arrow"
- split: test
path: "tzo_Latn/test/*.arrow"
- config_name: tsn_Hani
data_files:
- split: train
path: "tsn_Hani/train/*.arrow"
- split: dev
path: "tsn_Hani/dev/*.arrow"
- split: test
path: "tsn_Hani/test/*.arrow"
- config_name: aze_Zinh
data_files:
- split: train
path: "aze_Zinh/train/*.arrow"
- split: dev
path: "aze_Zinh/dev/*.arrow"
- split: test
path: "aze_Zinh/test/*.arrow"
- config_name: cuk_Latn
data_files:
- split: train
path: "cuk_Latn/train/*.arrow"
- split: dev
path: "cuk_Latn/dev/*.arrow"
- split: test
path: "cuk_Latn/test/*.arrow"
- config_name: uzb_Cyrl
data_files:
- split: train
path: "uzb_Cyrl/train/*.arrow"
- split: dev
path: "uzb_Cyrl/dev/*.arrow"
- split: test
path: "uzb_Cyrl/test/*.arrow"
- config_name: jav_Latn
data_files:
- split: train
path: "jav_Latn/train/*.arrow"
- split: dev
path: "jav_Latn/dev/*.arrow"
- split: test
path: "jav_Latn/test/*.arrow"
- config_name: rap_Latn
data_files:
- split: train
path: "rap_Latn/train/*.arrow"
- split: dev
path: "rap_Latn/dev/*.arrow"
- split: test
path: "rap_Latn/test/*.arrow"
- config_name: bak_Cyrl
data_files:
- split: train
path: "bak_Cyrl/train/*.arrow"
- split: dev
path: "bak_Cyrl/dev/*.arrow"
- split: test
path: "bak_Cyrl/test/*.arrow"
- config_name: por_Latn
data_files:
- split: train
path: "por_Latn/train/*.arrow"
- split: dev
path: "por_Latn/dev/*.arrow"
- split: test
path: "por_Latn/test/*.arrow"
- config_name: mlt_Hang
data_files:
- split: train
path: "mlt_Hang/train/*.arrow"
- split: dev
path: "mlt_Hang/dev/*.arrow"
- split: test
path: "mlt_Hang/test/*.arrow"
- config_name: quy_Latn
data_files:
- split: train
path: "quy_Latn/train/*.arrow"
- split: dev
path: "quy_Latn/dev/*.arrow"
- split: test
path: "quy_Latn/test/*.arrow"
- config_name: hnj_Latn
data_files:
- split: train
path: "hnj_Latn/train/*.arrow"
- split: dev
path: "hnj_Latn/dev/*.arrow"
- split: test
path: "hnj_Latn/test/*.arrow"
- config_name: ast_Latn
data_files:
- split: train
path: "ast_Latn/train/*.arrow"
- split: dev
path: "ast_Latn/dev/*.arrow"
- split: test
path: "ast_Latn/test/*.arrow"
- config_name: cos_Latn
data_files:
- split: train
path: "cos_Latn/train/*.arrow"
- split: dev
path: "cos_Latn/dev/*.arrow"
- split: test
path: "cos_Latn/test/*.arrow"
- config_name: fon_Latn
data_files:
- split: train
path: "fon_Latn/train/*.arrow"
- split: dev
path: "fon_Latn/dev/*.arrow"
- split: test
path: "fon_Latn/test/*.arrow"
- config_name: sna_Latn
data_files:
- split: train
path: "sna_Latn/train/*.arrow"
- split: dev
path: "sna_Latn/dev/*.arrow"
- split: test
path: "sna_Latn/test/*.arrow"
- config_name: dzo_Tibt
data_files:
- split: train
path: "dzo_Tibt/train/*.arrow"
- split: dev
path: "dzo_Tibt/dev/*.arrow"
- split: test
path: "dzo_Tibt/test/*.arrow"
- config_name: nob_Latn
data_files:
- split: train
path: "nob_Latn/train/*.arrow"
- split: dev
path: "nob_Latn/dev/*.arrow"
- split: test
path: "nob_Latn/test/*.arrow"
- config_name: nch_Latn
data_files:
- split: train
path: "nch_Latn/train/*.arrow"
- split: dev
path: "nch_Latn/dev/*.arrow"
- split: test
path: "nch_Latn/test/*.arrow"
- config_name: che_Cyrl
data_files:
- split: train
path: "che_Cyrl/train/*.arrow"
- split: dev
path: "che_Cyrl/dev/*.arrow"
- split: test
path: "che_Cyrl/test/*.arrow"
- config_name: ext_Latn
data_files:
- split: train
path: "ext_Latn/train/*.arrow"
- split: dev
path: "ext_Latn/dev/*.arrow"
- split: test
path: "ext_Latn/test/*.arrow"
- config_name: dtp_Latn
data_files:
- split: train
path: "dtp_Latn/train/*.arrow"
- split: dev
path: "dtp_Latn/dev/*.arrow"
- split: test
path: "dtp_Latn/test/*.arrow"
- config_name: yue_Hani
data_files:
- split: train
path: "yue_Hani/train/*.arrow"
- split: dev
path: "yue_Hani/dev/*.arrow"
- split: test
path: "yue_Hani/test/*.arrow"
- config_name: kbd_Cyrl
data_files:
- split: train
path: "kbd_Cyrl/train/*.arrow"
- split: dev
path: "kbd_Cyrl/dev/*.arrow"
- split: test
path: "kbd_Cyrl/test/*.arrow"
- config_name: mar_Deva
data_files:
- split: train
path: "mar_Deva/train/*.arrow"
- split: dev
path: "mar_Deva/dev/*.arrow"
- split: test
path: "mar_Deva/test/*.arrow"
- config_name: ron_Latn
data_files:
- split: train
path: "ron_Latn/train/*.arrow"
- split: dev
path: "ron_Latn/dev/*.arrow"
- split: test
path: "ron_Latn/test/*.arrow"
- config_name: acr_Latn
data_files:
- split: train
path: "acr_Latn/train/*.arrow"
- split: dev
path: "acr_Latn/dev/*.arrow"
- split: test
path: "acr_Latn/test/*.arrow"
- config_name: afb_Arab
data_files:
- split: train
path: "afb_Arab/train/*.arrow"
- split: dev
path: "afb_Arab/dev/*.arrow"
- split: test
path: "afb_Arab/test/*.arrow"
- config_name: mon_Hani
data_files:
- split: train
path: "mon_Hani/train/*.arrow"
- split: dev
path: "mon_Hani/dev/*.arrow"
- split: test
path: "mon_Hani/test/*.arrow"
- config_name: sqi_Latn
data_files:
- split: train
path: "sqi_Latn/train/*.arrow"
- split: dev
path: "sqi_Latn/dev/*.arrow"
- split: test
path: "sqi_Latn/test/*.arrow"
- config_name: eng_Latn
data_files:
- split: train
path: "eng_Latn/train/*.arrow"
- split: dev
path: "eng_Latn/dev/*.arrow"
- split: test
path: "eng_Latn/test/*.arrow"
- config_name: ksd_Latn
data_files:
- split: train
path: "ksd_Latn/train/*.arrow"
- split: dev
path: "ksd_Latn/dev/*.arrow"
- split: test
path: "ksd_Latn/test/*.arrow"
- config_name: rus_Cyrl
data_files:
- split: train
path: "rus_Cyrl/train/*.arrow"
- split: dev
path: "rus_Cyrl/dev/*.arrow"
- split: test
path: "rus_Cyrl/test/*.arrow"
- config_name: bcl_Latn
data_files:
- split: train
path: "bcl_Latn/train/*.arrow"
- split: dev
path: "bcl_Latn/dev/*.arrow"
- split: test
path: "bcl_Latn/test/*.arrow"
- config_name: ksh_Latn
data_files:
- split: train
path: "ksh_Latn/train/*.arrow"
- split: dev
path: "ksh_Latn/dev/*.arrow"
- split: test
path: "ksh_Latn/test/*.arrow"
- config_name: hin_Latn
data_files:
- split: train
path: "hin_Latn/train/*.arrow"
- split: dev
path: "hin_Latn/dev/*.arrow"
- split: test
path: "hin_Latn/test/*.arrow"
- config_name: myv_Cyrl
data_files:
- split: train
path: "myv_Cyrl/train/*.arrow"
- split: dev
path: "myv_Cyrl/dev/*.arrow"
- split: test
path: "myv_Cyrl/test/*.arrow"
- config_name: kjh_Cyrl
data_files:
- split: train
path: "kjh_Cyrl/train/*.arrow"
- split: dev
path: "kjh_Cyrl/dev/*.arrow"
- split: test
path: "kjh_Cyrl/test/*.arrow"
- config_name: sah_Cyrl
data_files:
- split: train
path: "sah_Cyrl/train/*.arrow"
- split: dev
path: "sah_Cyrl/dev/*.arrow"
- split: test
path: "sah_Cyrl/test/*.arrow"
- config_name: naq_Latn
data_files:
- split: train
path: "naq_Latn/train/*.arrow"
- split: dev
path: "naq_Latn/dev/*.arrow"
- split: test
path: "naq_Latn/test/*.arrow"
- config_name: tdt_Latn
data_files:
- split: train
path: "tdt_Latn/train/*.arrow"
- split: dev
path: "tdt_Latn/dev/*.arrow"
- split: test
path: "tdt_Latn/test/*.arrow"
- config_name: kac_Latn
data_files:
- split: train
path: "kac_Latn/train/*.arrow"
- split: dev
path: "kac_Latn/dev/*.arrow"
- split: test
path: "kac_Latn/test/*.arrow"
- config_name: cak_Latn
data_files:
- split: train
path: "cak_Latn/train/*.arrow"
- split: dev
path: "cak_Latn/dev/*.arrow"
- split: test
path: "cak_Latn/test/*.arrow"
- config_name: kir_Cyrl
data_files:
- split: train
path: "kir_Cyrl/train/*.arrow"
- split: dev
path: "kir_Cyrl/dev/*.arrow"
- split: test
path: "kir_Cyrl/test/*.arrow"
- config_name: mps_Latn
data_files:
- split: train
path: "mps_Latn/train/*.arrow"
- split: dev
path: "mps_Latn/dev/*.arrow"
- split: test
path: "mps_Latn/test/*.arrow"
- config_name: yid_Hebr
data_files:
- split: train
path: "yid_Hebr/train/*.arrow"
- split: dev
path: "yid_Hebr/dev/*.arrow"
- split: test
path: "yid_Hebr/test/*.arrow"
- config_name: mlt_Beng
data_files:
- split: train
path: "mlt_Beng/train/*.arrow"
- split: dev
path: "mlt_Beng/dev/*.arrow"
- split: test
path: "mlt_Beng/test/*.arrow"
- config_name: srn_Latn
data_files:
- split: train
path: "srn_Latn/train/*.arrow"
- split: dev
path: "srn_Latn/dev/*.arrow"
- split: test
path: "srn_Latn/test/*.arrow"
- config_name: div_Thaa
data_files:
- split: train
path: "div_Thaa/train/*.arrow"
- split: dev
path: "div_Thaa/dev/*.arrow"
- split: test
path: "div_Thaa/test/*.arrow"
- config_name: mlt_Kana
data_files:
- split: train
path: "mlt_Kana/train/*.arrow"
- split: dev
path: "mlt_Kana/dev/*.arrow"
- split: test
path: "mlt_Kana/test/*.arrow"
- config_name: mkd_Cyrl
data_files:
- split: train
path: "mkd_Cyrl/train/*.arrow"
- split: dev
path: "mkd_Cyrl/dev/*.arrow"
- split: test
path: "mkd_Cyrl/test/*.arrow"
- config_name: bre_Latn
data_files:
- split: train
path: "bre_Latn/train/*.arrow"
- split: dev
path: "bre_Latn/dev/*.arrow"
- split: test
path: "bre_Latn/test/*.arrow"
- config_name: tvl_Latn
data_files:
- split: train
path: "tvl_Latn/train/*.arrow"
- split: test
path: "tvl_Latn/test/*.arrow"
- config_name: ven_Latn
data_files:
- split: train
path: "ven_Latn/train/*.arrow"
- split: dev
path: "ven_Latn/dev/*.arrow"
- split: test
path: "ven_Latn/test/*.arrow"
- config_name: mlt_Mymr
data_files:
- split: train
path: "mlt_Mymr/train/*.arrow"
- split: dev
path: "mlt_Mymr/dev/*.arrow"
- split: test
path: "mlt_Mymr/test/*.arrow"
- config_name: wuu_Hani
data_files:
- split: train
path: "wuu_Hani/train/*.arrow"
- split: dev
path: "wuu_Hani/dev/*.arrow"
- split: test
path: "wuu_Hani/test/*.arrow"
- config_name: mwl_Latn
data_files:
- split: train
path: "mwl_Latn/train/*.arrow"
- split: dev
path: "mwl_Latn/dev/*.arrow"
- split: test
path: "mwl_Latn/test/*.arrow"
- config_name: miq_Latn
data_files:
- split: train
path: "miq_Latn/train/*.arrow"
- config_name: slv_Latn
data_files:
- split: train
path: "slv_Latn/train/*.arrow"
- split: dev
path: "slv_Latn/dev/*.arrow"
- split: test
path: "slv_Latn/test/*.arrow"
- config_name: hrv_Latn
data_files:
- split: train
path: "hrv_Latn/train/*.arrow"
- split: dev
path: "hrv_Latn/dev/*.arrow"
- split: test
path: "hrv_Latn/test/*.arrow"
- config_name: hmo_Latn
data_files:
- split: train
path: "hmo_Latn/train/*.arrow"
- split: dev
path: "hmo_Latn/dev/*.arrow"
- split: test
path: "hmo_Latn/test/*.arrow"
- config_name: som_Latn
data_files:
- split: train
path: "som_Latn/train/*.arrow"
- split: dev
path: "som_Latn/dev/*.arrow"
- split: test
path: "som_Latn/test/*.arrow"
- config_name: bod_Tibt
data_files:
- split: train
path: "bod_Tibt/train/*.arrow"
- split: dev
path: "bod_Tibt/dev/*.arrow"
- split: test
path: "bod_Tibt/test/*.arrow"
- config_name: pls_Latn
data_files:
- split: train
path: "pls_Latn/train/*.arrow"
- split: dev
path: "pls_Latn/dev/*.arrow"
- split: test
path: "pls_Latn/test/*.arrow"
- config_name: ile_Latn
data_files:
- split: train
path: "ile_Latn/train/*.arrow"
- split: dev
path: "ile_Latn/dev/*.arrow"
- split: test
path: "ile_Latn/test/*.arrow"
- config_name: luo_Latn
data_files:
- split: train
path: "luo_Latn/train/*.arrow"
- split: dev
path: "luo_Latn/dev/*.arrow"
- split: test
path: "luo_Latn/test/*.arrow"
- config_name: pus_Arab
data_files:
- split: train
path: "pus_Arab/train/*.arrow"
- split: dev
path: "pus_Arab/dev/*.arrow"
- split: test
path: "pus_Arab/test/*.arrow"
- config_name: fao_Latn
data_files:
- split: train
path: "fao_Latn/train/*.arrow"
- split: dev
path: "fao_Latn/dev/*.arrow"
- split: test
path: "fao_Latn/test/*.arrow"
- config_name: fas_Arab
data_files:
- split: train
path: "fas_Arab/train/*.arrow"
- split: dev
path: "fas_Arab/dev/*.arrow"
- split: test
path: "fas_Arab/test/*.arrow"
- config_name: swa_Latn
data_files:
- split: train
path: "swa_Latn/train/*.arrow"
- split: dev
path: "swa_Latn/dev/*.arrow"
- split: test
path: "swa_Latn/test/*.arrow"
- config_name: mlt_Hebr
data_files:
- split: train
path: "mlt_Hebr/train/*.arrow"
- split: dev
path: "mlt_Hebr/dev/*.arrow"
- split: test
path: "mlt_Hebr/test/*.arrow"
- config_name: ary_Arab
data_files:
- split: train
path: "ary_Arab/train/*.arrow"
- split: dev
path: "ary_Arab/dev/*.arrow"
- split: test
path: "ary_Arab/test/*.arrow"
- config_name: hus_Latn
data_files:
- split: train
path: "hus_Latn/train/*.arrow"
- split: dev
path: "hus_Latn/dev/*.arrow"
- split: test
path: "hus_Latn/test/*.arrow"
- config_name: ote_Latn
data_files:
- split: train
path: "ote_Latn/train/*.arrow"
- split: dev
path: "ote_Latn/dev/*.arrow"
- split: test
path: "ote_Latn/test/*.arrow"
- config_name: ilo_Latn
data_files:
- split: train
path: "ilo_Latn/train/*.arrow"
- split: dev
path: "ilo_Latn/dev/*.arrow"
- split: test
path: "ilo_Latn/test/*.arrow"
- config_name: abk_Cyrl
data_files:
- split: train
path: "abk_Cyrl/train/*.arrow"
- split: dev
path: "abk_Cyrl/dev/*.arrow"
- split: test
path: "abk_Cyrl/test/*.arrow"
- config_name: bqc_Latn
data_files:
- split: train
path: "bqc_Latn/train/*.arrow"
- split: dev
path: "bqc_Latn/dev/*.arrow"
- split: test
path: "bqc_Latn/test/*.arrow"
- config_name: mlt_Taml
data_files:
- split: train
path: "mlt_Taml/train/*.arrow"
- split: dev
path: "mlt_Taml/dev/*.arrow"
- split: test
path: "mlt_Taml/test/*.arrow"
- config_name: hil_Latn
data_files:
- split: train
path: "hil_Latn/train/*.arrow"
- config_name: pon_Latn
data_files:
- split: train
path: "pon_Latn/train/*.arrow"
- split: dev
path: "pon_Latn/dev/*.arrow"
- split: test
path: "pon_Latn/test/*.arrow"
- config_name: zul_Latn
data_files:
- split: train
path: "zul_Latn/train/*.arrow"
- split: dev
path: "zul_Latn/dev/*.arrow"
- split: test
path: "zul_Latn/test/*.arrow"
- config_name: als_Latn
data_files:
- split: train
path: "als_Latn/train/*.arrow"
- split: dev
path: "als_Latn/dev/*.arrow"
- split: test
path: "als_Latn/test/*.arrow"
- config_name: pes_Arab
data_files:
- split: train
path: "pes_Arab/train/*.arrow"
- split: dev
path: "pes_Arab/dev/*.arrow"
- split: test
path: "pes_Arab/test/*.arrow"
- config_name: bpy_Beng
data_files:
- split: train
path: "bpy_Beng/train/*.arrow"
- split: dev
path: "bpy_Beng/dev/*.arrow"
- split: test
path: "bpy_Beng/test/*.arrow"
- config_name: bos_Latn
data_files:
- split: train
path: "bos_Latn/train/*.arrow"
- split: dev
path: "bos_Latn/dev/*.arrow"
- split: test
path: "bos_Latn/test/*.arrow"
- config_name: sot_Latn
data_files:
- split: train
path: "sot_Latn/train/*.arrow"
- split: dev
path: "sot_Latn/dev/*.arrow"
- split: test
path: "sot_Latn/test/*.arrow"
- config_name: lin_Latn
data_files:
- split: train
path: "lin_Latn/train/*.arrow"
- split: dev
path: "lin_Latn/dev/*.arrow"
- split: test
path: "lin_Latn/test/*.arrow"
- config_name: tuk_Cyrl
data_files:
- split: train
path: "tuk_Cyrl/train/*.arrow"
- split: dev
path: "tuk_Cyrl/dev/*.arrow"
- config_name: gla_Latn
data_files:
- split: train
path: "gla_Latn/train/*.arrow"
- split: dev
path: "gla_Latn/dev/*.arrow"
- split: test
path: "gla_Latn/test/*.arrow"
- config_name: wln_Latn
data_files:
- split: train
path: "wln_Latn/train/*.arrow"
- split: dev
path: "wln_Latn/dev/*.arrow"
- split: test
path: "wln_Latn/test/*.arrow"
- config_name: apc_Arab
data_files:
- split: train
path: "apc_Arab/train/*.arrow"
- split: dev
path: "apc_Arab/dev/*.arrow"
- split: test
path: "apc_Arab/test/*.arrow"
- config_name: aze_Hira
data_files:
- split: train
path: "aze_Hira/train/*.arrow"
- split: dev
path: "aze_Hira/dev/*.arrow"
- split: test
path: "aze_Hira/test/*.arrow"
- config_name: hin_Deva
data_files:
- split: train
path: "hin_Deva/train/*.arrow"
- split: dev
path: "hin_Deva/dev/*.arrow"
- split: test
path: "hin_Deva/test/*.arrow"
- config_name: hye_Armn
data_files:
- split: train
path: "hye_Armn/train/*.arrow"
- split: dev
path: "hye_Armn/dev/*.arrow"
- split: test
path: "hye_Armn/test/*.arrow"
- config_name: tir_Ethi
data_files:
- split: train
path: "tir_Ethi/train/*.arrow"
- split: dev
path: "tir_Ethi/dev/*.arrow"
- split: test
path: "tir_Ethi/test/*.arrow"
- config_name: aze_Ethi
data_files:
- split: train
path: "aze_Ethi/train/*.arrow"
- split: dev
path: "aze_Ethi/dev/*.arrow"
- split: test
path: "aze_Ethi/test/*.arrow"
- config_name: pap_Latn
data_files:
- split: train
path: "pap_Latn/train/*.arrow"
- split: dev
path: "pap_Latn/dev/*.arrow"
- split: test
path: "pap_Latn/test/*.arrow"
- config_name: mlt_Ethi
data_files:
- split: train
path: "mlt_Ethi/train/*.arrow"
- split: dev
path: "mlt_Ethi/dev/*.arrow"
- split: test
path: "mlt_Ethi/test/*.arrow"
- config_name: gcf_Latn
data_files:
- split: train
path: "gcf_Latn/train/*.arrow"
- split: dev
path: "gcf_Latn/dev/*.arrow"
- split: test
path: "gcf_Latn/test/*.arrow"
- config_name: cjk_Latn
data_files:
- split: train
path: "cjk_Latn/train/*.arrow"
- split: dev
path: "cjk_Latn/dev/*.arrow"
- split: test
path: "cjk_Latn/test/*.arrow"
- config_name: pcd_Latn
data_files:
- split: train
path: "pcd_Latn/train/*.arrow"
- split: dev
path: "pcd_Latn/dev/*.arrow"
- split: test
path: "pcd_Latn/test/*.arrow"
- config_name: tur_Latn
data_files:
- split: train
path: "tur_Latn/train/*.arrow"
- split: dev
path: "tur_Latn/dev/*.arrow"
- split: test
path: "tur_Latn/test/*.arrow"
- config_name: kon_Latn
data_files:
- split: train
path: "kon_Latn/train/*.arrow"
- split: dev
path: "kon_Latn/dev/*.arrow"
- split: test
path: "kon_Latn/test/*.arrow"
- config_name: csy_Latn
data_files:
- split: train
path: "csy_Latn/train/*.arrow"
- split: dev
path: "csy_Latn/dev/*.arrow"
- split: test
path: "csy_Latn/test/*.arrow"
- config_name: xho_Latn
data_files:
- split: train
path: "xho_Latn/train/*.arrow"
- split: dev
path: "xho_Latn/dev/*.arrow"
- split: test
path: "xho_Latn/test/*.arrow"
- config_name: guc_Latn
data_files:
- split: train
path: "guc_Latn/train/*.arrow"
- config_name: aka_Latn
data_files:
- split: train
path: "aka_Latn/train/*.arrow"
- split: dev
path: "aka_Latn/dev/*.arrow"
- split: test
path: "aka_Latn/test/*.arrow"
- config_name: kea_Latn
data_files:
- split: train
path: "kea_Latn/train/*.arrow"
- split: dev
path: "kea_Latn/dev/*.arrow"
- split: test
path: "kea_Latn/test/*.arrow"
- config_name: bar_Latn
data_files:
- split: train
path: "bar_Latn/train/*.arrow"
- split: dev
path: "bar_Latn/dev/*.arrow"
- split: test
path: "bar_Latn/test/*.arrow"
- config_name: sme_Latn
data_files:
- split: train
path: "sme_Latn/train/*.arrow"
- split: dev
path: "sme_Latn/dev/*.arrow"
- split: test
path: "sme_Latn/test/*.arrow"
- config_name: aze_Hang
data_files:
- split: train
path: "aze_Hang/train/*.arrow"
- split: dev
path: "aze_Hang/dev/*.arrow"
- split: test
path: "aze_Hang/test/*.arrow"
- config_name: csb_Latn
data_files:
- split: train
path: "csb_Latn/train/*.arrow"
- split: dev
path: "csb_Latn/dev/*.arrow"
- split: test
path: "csb_Latn/test/*.arrow"
- config_name: bak_Latn
data_files:
- split: train
path: "bak_Latn/train/*.arrow"
- split: dev
path: "bak_Latn/dev/*.arrow"
- split: test
path: "bak_Latn/test/*.arrow"
- config_name: djk_Latn
data_files:
- split: train
path: "djk_Latn/train/*.arrow"
- split: dev
path: "djk_Latn/dev/*.arrow"
- split: test
path: "djk_Latn/test/*.arrow"
- config_name: xav_Latn
data_files:
- split: train
path: "xav_Latn/train/*.arrow"
- split: dev
path: "xav_Latn/dev/*.arrow"
- split: test
path: "xav_Latn/test/*.arrow"
- config_name: oci_Latn
data_files:
- split: train
path: "oci_Latn/train/*.arrow"
- split: dev
path: "oci_Latn/dev/*.arrow"
- split: test
path: "oci_Latn/test/*.arrow"
- config_name: acm_Arab
data_files:
- split: train
path: "acm_Arab/train/*.arrow"
- split: dev
path: "acm_Arab/dev/*.arrow"
- split: test
path: "acm_Arab/test/*.arrow"
- config_name: rmy_Cyrl
data_files:
- split: train
path: "rmy_Cyrl/train/*.arrow"
- config_name: krc_Cyrl
data_files:
- split: train
path: "krc_Cyrl/train/*.arrow"
- split: dev
path: "krc_Cyrl/dev/*.arrow"
- split: test
path: "krc_Cyrl/test/*.arrow"
- config_name: cym_Latn
data_files:
- split: train
path: "cym_Latn/train/*.arrow"
- split: dev
path: "cym_Latn/dev/*.arrow"
- split: test
path: "cym_Latn/test/*.arrow"
- config_name: lus_Latn
data_files:
- split: train
path: "lus_Latn/train/*.arrow"
- split: dev
path: "lus_Latn/dev/*.arrow"
- split: test
path: "lus_Latn/test/*.arrow"
- config_name: ngu_Latn
data_files:
- split: train
path: "ngu_Latn/train/*.arrow"
- split: dev
path: "ngu_Latn/dev/*.arrow"
- split: test
path: "ngu_Latn/test/*.arrow"
- config_name: yom_Latn
data_files:
- split: train
path: "yom_Latn/train/*.arrow"
- split: dev
path: "yom_Latn/dev/*.arrow"
- split: test
path: "yom_Latn/test/*.arrow"
- config_name: tam_Taml
data_files:
- split: train
path: "tam_Taml/train/*.arrow"
- split: dev
path: "tam_Taml/dev/*.arrow"
- split: test
path: "tam_Taml/test/*.arrow"
- config_name: ajp_Arab
data_files:
- split: train
path: "ajp_Arab/train/*.arrow"
- split: dev
path: "ajp_Arab/dev/*.arrow"
- split: test
path: "ajp_Arab/test/*.arrow"
- config_name: epo_Latn
data_files:
- split: train
path: "epo_Latn/train/*.arrow"
- split: dev
path: "epo_Latn/dev/*.arrow"
- split: test
path: "epo_Latn/test/*.arrow"
- config_name: fra_Latn
data_files:
- split: train
path: "fra_Latn/train/*.arrow"
- split: dev
path: "fra_Latn/dev/*.arrow"
- split: test
path: "fra_Latn/test/*.arrow"
- config_name: ita_Latn
data_files:
- split: train
path: "ita_Latn/train/*.arrow"
- split: dev
path: "ita_Latn/dev/*.arrow"
- split: test
path: "ita_Latn/test/*.arrow"
- config_name: seh_Latn
data_files:
- split: train
path: "seh_Latn/train/*.arrow"
- split: dev
path: "seh_Latn/dev/*.arrow"
- split: test
path: "seh_Latn/test/*.arrow"
- config_name: hbs_Latn
data_files:
- split: train
path: "hbs_Latn/train/*.arrow"
- split: dev
path: "hbs_Latn/dev/*.arrow"
- split: test
path: "hbs_Latn/test/*.arrow"
- config_name: uzn_Cyrl
data_files:
- split: train
path: "uzn_Cyrl/train/*.arrow"
- split: dev
path: "uzn_Cyrl/dev/*.arrow"
- split: test
path: "uzn_Cyrl/test/*.arrow"
- config_name: ksw_Mymr
data_files:
- split: train
path: "ksw_Mymr/train/*.arrow"
- config_name: pms_Latn
data_files:
- split: train
path: "pms_Latn/train/*.arrow"
- split: dev
path: "pms_Latn/dev/*.arrow"
- split: test
path: "pms_Latn/test/*.arrow"
- config_name: zlm_Latn
data_files:
- split: train
path: "zlm_Latn/train/*.arrow"
- split: dev
path: "zlm_Latn/dev/*.arrow"
- split: test
path: "zlm_Latn/test/*.arrow"
- config_name: qub_Latn
data_files:
- split: train
path: "qub_Latn/train/*.arrow"
- split: dev
path: "qub_Latn/dev/*.arrow"
- config_name: arg_Latn
data_files:
- split: train
path: "arg_Latn/train/*.arrow"
- split: dev
path: "arg_Latn/dev/*.arrow"
- split: test
path: "arg_Latn/test/*.arrow"
- config_name: kaa_Cyrl
data_files:
- split: train
path: "kaa_Cyrl/train/*.arrow"
- split: dev
path: "kaa_Cyrl/dev/*.arrow"
- split: test
path: "kaa_Cyrl/test/*.arrow"
- config_name: toj_Latn
data_files:
- split: train
path: "toj_Latn/train/*.arrow"
- split: dev
path: "toj_Latn/dev/*.arrow"
- split: test
path: "toj_Latn/test/*.arrow"
- config_name: aze_Grek
data_files:
- split: train
path: "aze_Grek/train/*.arrow"
- split: dev
path: "aze_Grek/dev/*.arrow"
- split: test
path: "aze_Grek/test/*.arrow"
- config_name: guj_Cyrl
data_files:
- split: train
path: "guj_Cyrl/train/*.arrow"
- split: dev
path: "guj_Cyrl/dev/*.arrow"
- split: test
path: "guj_Cyrl/test/*.arrow"
- config_name: kab_Latn
data_files:
- split: train
path: "kab_Latn/train/*.arrow"
- split: dev
path: "kab_Latn/dev/*.arrow"
- split: test
path: "kab_Latn/test/*.arrow"
- config_name: pan_Guru
data_files:
- split: train
path: "pan_Guru/train/*.arrow"
- split: dev
path: "pan_Guru/dev/*.arrow"
- split: test
path: "pan_Guru/test/*.arrow"
- config_name: nan_Latn
data_files:
- split: train
path: "nan_Latn/train/*.arrow"
- split: dev
path: "nan_Latn/dev/*.arrow"
- split: test
path: "nan_Latn/test/*.arrow"
- config_name: aze_Latn
data_files:
- split: train
path: "aze_Latn/train/*.arrow"
- split: dev
path: "aze_Latn/dev/*.arrow"
- split: test
path: "aze_Latn/test/*.arrow"
- config_name: ara_Arab
data_files:
- split: train
path: "ara_Arab/train/*.arrow"
- split: dev
path: "ara_Arab/dev/*.arrow"
- split: test
path: "ara_Arab/test/*.arrow"
- config_name: aze_Mymr
data_files:
- split: train
path: "aze_Mymr/train/*.arrow"
- split: dev
path: "aze_Mymr/dev/*.arrow"
- split: test
path: "aze_Mymr/test/*.arrow"
- config_name: meu_Latn
data_files:
- split: train
path: "meu_Latn/train/*.arrow"
- split: dev
path: "meu_Latn/dev/*.arrow"
- split: test
path: "meu_Latn/test/*.arrow"
- config_name: mon_Arab
data_files:
- split: train
path: "mon_Arab/train/*.arrow"
- split: dev
path: "mon_Arab/dev/*.arrow"
- split: test
path: "mon_Arab/test/*.arrow"
- config_name: lvs_Latn
data_files:
- split: train
path: "lvs_Latn/train/*.arrow"
- split: dev
path: "lvs_Latn/dev/*.arrow"
- split: test
path: "lvs_Latn/test/*.arrow"
- config_name: nbl_Latn
data_files:
- split: train
path: "nbl_Latn/train/*.arrow"
- split: dev
path: "nbl_Latn/dev/*.arrow"
- split: test
path: "nbl_Latn/test/*.arrow"
- config_name: crh_Latn
data_files:
- split: train
path: "crh_Latn/train/*.arrow"
- split: dev
path: "crh_Latn/dev/*.arrow"
- split: test
path: "crh_Latn/test/*.arrow"
- config_name: kbp_Latn
data_files:
- split: train
path: "kbp_Latn/train/*.arrow"
- split: dev
path: "kbp_Latn/dev/*.arrow"
- split: test
path: "kbp_Latn/test/*.arrow"
- config_name: tgl_Latn
data_files:
- split: train
path: "tgl_Latn/train/*.arrow"
- split: dev
path: "tgl_Latn/dev/*.arrow"
- split: test
path: "tgl_Latn/test/*.arrow"
- config_name: kmb_Latn
data_files:
- split: train
path: "kmb_Latn/train/*.arrow"
- split: dev
path: "kmb_Latn/dev/*.arrow"
- split: test
path: "kmb_Latn/test/*.arrow"
- config_name: hun_Latn
data_files:
- split: train
path: "hun_Latn/train/*.arrow"
- split: dev
path: "hun_Latn/dev/*.arrow"
- split: test
path: "hun_Latn/test/*.arrow"
- config_name: aze_Thai
data_files:
- split: train
path: "aze_Thai/train/*.arrow"
- split: dev
path: "aze_Thai/dev/*.arrow"
- split: test
path: "aze_Thai/test/*.arrow"
- config_name: yao_Latn
data_files:
- split: train
path: "yao_Latn/train/*.arrow"
- split: dev
path: "yao_Latn/dev/*.arrow"
- split: test
path: "yao_Latn/test/*.arrow"
- config_name: arn_Latn
data_files:
- split: train
path: "arn_Latn/train/*.arrow"
- split: dev
path: "arn_Latn/dev/*.arrow"
- split: test
path: "arn_Latn/test/*.arrow"
- config_name: jbo_Latn
data_files:
- split: train
path: "jbo_Latn/train/*.arrow"
- split: dev
path: "jbo_Latn/dev/*.arrow"
- split: test
path: "jbo_Latn/test/*.arrow"
- config_name: mzn_Arab
data_files:
- split: train
path: "mzn_Arab/train/*.arrow"
- split: dev
path: "mzn_Arab/dev/*.arrow"
- split: test
path: "mzn_Arab/test/*.arrow"
- config_name: lzh_Hani
data_files:
- split: train
path: "lzh_Hani/train/*.arrow"
- split: dev
path: "lzh_Hani/dev/*.arrow"
- split: test
path: "lzh_Hani/test/*.arrow"
- config_name: heb_Hebr
data_files:
- split: train
path: "heb_Hebr/train/*.arrow"
- split: dev
path: "heb_Hebr/dev/*.arrow"
- split: test
path: "heb_Hebr/test/*.arrow"
- config_name: bjn_Latn
data_files:
- split: train
path: "bjn_Latn/train/*.arrow"
- split: dev
path: "bjn_Latn/dev/*.arrow"
- split: test
path: "bjn_Latn/test/*.arrow"
- config_name: gug_Latn
data_files:
- split: train
path: "gug_Latn/train/*.arrow"
- split: dev
path: "gug_Latn/dev/*.arrow"
- split: test
path: "gug_Latn/test/*.arrow"
- config_name: mlt_Hira
data_files:
- split: train
path: "mlt_Hira/train/*.arrow"
- split: dev
path: "mlt_Hira/dev/*.arrow"
- split: test
path: "mlt_Hira/test/*.arrow"
- config_name: swc_Latn
data_files:
- split: train
path: "swc_Latn/train/*.arrow"
- split: dev
path: "swc_Latn/dev/*.arrow"
- split: test
path: "swc_Latn/test/*.arrow"
- config_name: yor_Latn
data_files:
- split: train
path: "yor_Latn/train/*.arrow"
- split: dev
path: "yor_Latn/dev/*.arrow"
- split: test
path: "yor_Latn/test/*.arrow"
- config_name: ban_Latn
data_files:
- split: train
path: "ban_Latn/train/*.arrow"
- split: dev
path: "ban_Latn/dev/*.arrow"
- split: test
path: "ban_Latn/test/*.arrow"
- config_name: aze_Guru
data_files:
- split: train
path: "aze_Guru/train/*.arrow"
- split: dev
path: "aze_Guru/dev/*.arrow"
- split: test
path: "aze_Guru/test/*.arrow"
- config_name: tlh_Latn
data_files:
- split: train
path: "tlh_Latn/train/*.arrow"
- split: dev
path: "tlh_Latn/dev/*.arrow"
- split: test
path: "tlh_Latn/test/*.arrow"
- config_name: chv_Cyrl
data_files:
- split: train
path: "chv_Cyrl/train/*.arrow"
- split: dev
path: "chv_Cyrl/dev/*.arrow"
- split: test
path: "chv_Cyrl/test/*.arrow"
- config_name: sin_Sinh
data_files:
- split: train
path: "sin_Sinh/train/*.arrow"
- split: dev
path: "sin_Sinh/dev/*.arrow"
- split: test
path: "sin_Sinh/test/*.arrow"
- config_name: aze_Gujr
data_files:
- split: train
path: "aze_Gujr/train/*.arrow"
- split: dev
path: "aze_Gujr/dev/*.arrow"
- split: test
path: "aze_Gujr/test/*.arrow"
- config_name: ind_Latn
data_files:
- split: train
path: "ind_Latn/train/*.arrow"
- split: dev
path: "ind_Latn/dev/*.arrow"
- split: test
path: "ind_Latn/test/*.arrow"
- config_name: amh_Ethi
data_files:
- split: train
path: "amh_Ethi/train/*.arrow"
- split: dev
path: "amh_Ethi/dev/*.arrow"
- split: test
path: "amh_Ethi/test/*.arrow"
- config_name: zea_Latn
data_files:
- split: train
path: "zea_Latn/train/*.arrow"
- split: dev
path: "zea_Latn/dev/*.arrow"
- split: test
path: "zea_Latn/test/*.arrow"
- config_name: kpg_Latn
data_files:
- split: train
path: "kpg_Latn/train/*.arrow"
- split: dev
path: "kpg_Latn/dev/*.arrow"
- split: test
path: "kpg_Latn/test/*.arrow"
- config_name: glk_Arab
data_files:
- split: train
path: "glk_Arab/train/*.arrow"
- split: dev
path: "glk_Arab/dev/*.arrow"
- split: test
path: "glk_Arab/test/*.arrow"
- config_name: crh_Cyrl
data_files:
- split: train
path: "crh_Cyrl/train/*.arrow"
- split: dev
path: "crh_Cyrl/dev/*.arrow"
- split: test
path: "crh_Cyrl/test/*.arrow"
- config_name: nyu_Latn
data_files:
- split: train
path: "nyu_Latn/train/*.arrow"
- config_name: aze_Beng
data_files:
- split: train
path: "aze_Beng/train/*.arrow"
- split: dev
path: "aze_Beng/dev/*.arrow"
- split: test
path: "aze_Beng/test/*.arrow"
- config_name: ibo_Latn
data_files:
- split: train
path: "ibo_Latn/train/*.arrow"
- split: dev
path: "ibo_Latn/dev/*.arrow"
- split: test
path: "ibo_Latn/test/*.arrow"
- config_name: msa_Latn
data_files:
- split: train
path: "msa_Latn/train/*.arrow"
- split: dev
path: "msa_Latn/dev/*.arrow"
- split: test
path: "msa_Latn/test/*.arrow"
- config_name: prs_Arab
data_files:
- split: train
path: "prs_Arab/train/*.arrow"
- split: dev
path: "prs_Arab/dev/*.arrow"
- split: test
path: "prs_Arab/test/*.arrow"
- config_name: nap_Latn
data_files:
- split: train
path: "nap_Latn/train/*.arrow"
- split: dev
path: "nap_Latn/dev/*.arrow"
- split: test
path: "nap_Latn/test/*.arrow"
- config_name: bik_Latn
data_files:
- split: train
path: "bik_Latn/train/*.arrow"
- split: dev
path: "bik_Latn/dev/*.arrow"
- split: test
path: "bik_Latn/test/*.arrow"
- config_name: srp_Cyrl
data_files:
- split: train
path: "srp_Cyrl/train/*.arrow"
- split: dev
path: "srp_Cyrl/dev/*.arrow"
- split: test
path: "srp_Cyrl/test/*.arrow"
- config_name: lao_Laoo
data_files:
- split: train
path: "lao_Laoo/train/*.arrow"
- split: dev
path: "lao_Laoo/dev/*.arrow"
- split: test
path: "lao_Laoo/test/*.arrow"
- config_name: kom_Cyrl
data_files:
- split: train
path: "kom_Cyrl/train/*.arrow"
- split: dev
path: "kom_Cyrl/dev/*.arrow"
- split: test
path: "kom_Cyrl/test/*.arrow"
- config_name: nde_Latn
data_files:
- split: train
path: "nde_Latn/train/*.arrow"
- split: dev
path: "nde_Latn/dev/*.arrow"
- split: test
path: "nde_Latn/test/*.arrow"
- config_name: hui_Latn
data_files:
- split: train
path: "hui_Latn/train/*.arrow"
- split: dev
path: "hui_Latn/dev/*.arrow"
- split: test
path: "hui_Latn/test/*.arrow"
- config_name: uig_Latn
data_files:
- split: train
path: "uig_Latn/train/*.arrow"
- split: dev
path: "uig_Latn/dev/*.arrow"
- split: test
path: "uig_Latn/test/*.arrow"
- config_name: new_Deva
data_files:
- split: train
path: "new_Deva/train/*.arrow"
- split: dev
path: "new_Deva/dev/*.arrow"
- split: test
path: "new_Deva/test/*.arrow"
- config_name: kur_Arab
data_files:
- split: train
path: "kur_Arab/train/*.arrow"
- split: dev
path: "kur_Arab/dev/*.arrow"
- split: test
path: "kur_Arab/test/*.arrow"
- config_name: sco_Latn
data_files:
- split: train
path: "sco_Latn/train/*.arrow"
- split: dev
path: "sco_Latn/dev/*.arrow"
- split: test
path: "sco_Latn/test/*.arrow"
- config_name: ayr_Latn
data_files:
- split: train
path: "ayr_Latn/train/*.arrow"
- split: dev
path: "ayr_Latn/dev/*.arrow"
- split: test
path: "ayr_Latn/test/*.arrow"
- config_name: suz_Deva
data_files:
- split: train
path: "suz_Deva/train/*.arrow"
- split: dev
path: "suz_Deva/dev/*.arrow"
- split: test
path: "suz_Deva/test/*.arrow"
- config_name: wal_Latn
data_files:
- split: train
path: "wal_Latn/train/*.arrow"
- split: dev
path: "wal_Latn/dev/*.arrow"
- split: test
path: "wal_Latn/test/*.arrow"
- config_name: mlt_Latn
data_files:
- split: train
path: "mlt_Latn/train/*.arrow"
- split: dev
path: "mlt_Latn/dev/*.arrow"
- split: test
path: "mlt_Latn/test/*.arrow"
- config_name: asm_Beng
data_files:
- split: train
path: "asm_Beng/train/*.arrow"
- split: dev
path: "asm_Beng/dev/*.arrow"
- split: test
path: "asm_Beng/test/*.arrow"
- config_name: aze_Syrc
data_files:
- split: train
path: "aze_Syrc/train/*.arrow"
- split: dev
path: "aze_Syrc/dev/*.arrow"
- split: test
path: "aze_Syrc/test/*.arrow"
- config_name: san_Deva
data_files:
- split: train
path: "san_Deva/train/*.arrow"
- split: dev
path: "san_Deva/dev/*.arrow"
- split: test
path: "san_Deva/test/*.arrow"
- config_name: kaz_Cyrl
data_files:
- split: train
path: "kaz_Cyrl/train/*.arrow"
- split: dev
path: "kaz_Cyrl/dev/*.arrow"
- split: test
path: "kaz_Cyrl/test/*.arrow"
- config_name: iba_Latn
data_files:
- split: train
path: "iba_Latn/train/*.arrow"
- config_name: tuk_Latn
data_files:
- split: train
path: "tuk_Latn/train/*.arrow"
- split: dev
path: "tuk_Latn/dev/*.arrow"
- split: test
path: "tuk_Latn/test/*.arrow"
- config_name: nso_Latn
data_files:
- split: train
path: "nso_Latn/train/*.arrow"
- split: dev
path: "nso_Latn/dev/*.arrow"
- split: test
path: "nso_Latn/test/*.arrow"
- config_name: aze_Geor
data_files:
- split: train
path: "aze_Geor/train/*.arrow"
- split: dev
path: "aze_Geor/dev/*.arrow"
- split: test
path: "aze_Geor/test/*.arrow"
- config_name: run_Latn
data_files:
- split: train
path: "run_Latn/train/*.arrow"
- split: dev
path: "run_Latn/dev/*.arrow"
- split: test
path: "run_Latn/test/*.arrow"
- config_name: ctu_Latn
data_files:
- split: train
path: "ctu_Latn/train/*.arrow"
- split: dev
path: "ctu_Latn/dev/*.arrow"
- split: test
path: "ctu_Latn/test/*.arrow"
- config_name: bam_Latn
data_files:
- split: train
path: "bam_Latn/train/*.arrow"
- split: dev
path: "bam_Latn/dev/*.arrow"
- split: test
path: "bam_Latn/test/*.arrow"
- config_name: fin_Latn
data_files:
- split: train
path: "fin_Latn/train/*.arrow"
- split: dev
path: "fin_Latn/dev/*.arrow"
- split: test
path: "fin_Latn/test/*.arrow"
- config_name: gor_Latn
data_files:
- split: train
path: "gor_Latn/train/*.arrow"
- split: dev
path: "gor_Latn/dev/*.arrow"
- split: test
path: "gor_Latn/test/*.arrow"
- config_name: kmr_Latn
data_files:
- split: train
path: "kmr_Latn/train/*.arrow"
- split: dev
path: "kmr_Latn/dev/*.arrow"
- split: test
path: "kmr_Latn/test/*.arrow"
- config_name: ben_Beng
data_files:
- split: train
path: "ben_Beng/train/*.arrow"
- split: dev
path: "ben_Beng/dev/*.arrow"
- split: test
path: "ben_Beng/test/*.arrow"
- config_name: pag_Latn
data_files:
- split: train
path: "pag_Latn/train/*.arrow"
- split: dev
path: "pag_Latn/dev/*.arrow"
- split: test
path: "pag_Latn/test/*.arrow"
- config_name: niu_Latn
data_files:
- split: train
path: "niu_Latn/train/*.arrow"
- config_name: xmf_Geor
data_files:
- split: train
path: "xmf_Geor/train/*.arrow"
- split: dev
path: "xmf_Geor/dev/*.arrow"
- split: test
path: "xmf_Geor/test/*.arrow"
- config_name: ekk_Latn
data_files:
- split: train
path: "ekk_Latn/train/*.arrow"
- split: dev
path: "ekk_Latn/dev/*.arrow"
- split: test
path: "ekk_Latn/test/*.arrow"
- config_name: lmo_Latn
data_files:
- split: train
path: "lmo_Latn/train/*.arrow"
- split: dev
path: "lmo_Latn/dev/*.arrow"
- split: test
path: "lmo_Latn/test/*.arrow"
- config_name: mhr_Cyrl
data_files:
- split: train
path: "mhr_Cyrl/train/*.arrow"
- split: dev
path: "mhr_Cyrl/dev/*.arrow"
- split: test
path: "mhr_Cyrl/test/*.arrow"
- config_name: plt_Latn
data_files:
- split: train
path: "plt_Latn/train/*.arrow"
- split: dev
path: "plt_Latn/dev/*.arrow"
- split: test
path: "plt_Latn/test/*.arrow"
- config_name: qvi_Latn
data_files:
- split: train
path: "qvi_Latn/train/*.arrow"
- split: dev
path: "qvi_Latn/dev/*.arrow"
- split: test
path: "qvi_Latn/test/*.arrow"
- config_name: mlt_Zinh
data_files:
- split: train
path: "mlt_Zinh/train/*.arrow"
- split: dev
path: "mlt_Zinh/dev/*.arrow"
- split: test
path: "mlt_Zinh/test/*.arrow"
- config_name: roh_Latn
data_files:
- split: train
path: "roh_Latn/train/*.arrow"
- split: dev
path: "roh_Latn/dev/*.arrow"
- split: test
path: "roh_Latn/test/*.arrow"
- config_name: mah_Latn
data_files:
- split: train
path: "mah_Latn/train/*.arrow"
- config_name: npi_Deva
data_files:
- split: train
path: "npi_Deva/train/*.arrow"
- split: dev
path: "npi_Deva/dev/*.arrow"
- split: test
path: "npi_Deva/test/*.arrow"
- config_name: guj_Telu
data_files:
- split: train
path: "guj_Telu/train/*.arrow"
- split: dev
path: "guj_Telu/dev/*.arrow"
- split: test
path: "guj_Telu/test/*.arrow"
- config_name: tok_Latn
data_files:
- split: train
path: "tok_Latn/train/*.arrow"
- split: dev
path: "tok_Latn/dev/*.arrow"
- split: test
path: "tok_Latn/test/*.arrow"
- config_name: eml_Latn
data_files:
- split: train
path: "eml_Latn/train/*.arrow"
- split: dev
path: "eml_Latn/dev/*.arrow"
- split: test
path: "eml_Latn/test/*.arrow"
- config_name: pnb_Arab
data_files:
- split: train
path: "pnb_Arab/train/*.arrow"
- split: dev
path: "pnb_Arab/dev/*.arrow"
- split: test
path: "pnb_Arab/test/*.arrow"
- config_name: tsn_Hira
data_files:
- split: train
path: "tsn_Hira/train/*.arrow"
- split: dev
path: "tsn_Hira/dev/*.arrow"
- split: test
path: "tsn_Hira/test/*.arrow"
- config_name: nav_Latn
data_files:
- split: train
path: "nav_Latn/train/*.arrow"
- split: dev
path: "nav_Latn/dev/*.arrow"
- split: test
path: "nav_Latn/test/*.arrow"
- config_name: hyw_Latn
data_files:
- split: train
path: "hyw_Latn/train/*.arrow"
- config_name: cat_Latn
data_files:
- split: train
path: "cat_Latn/train/*.arrow"
- split: dev
path: "cat_Latn/dev/*.arrow"
- split: test
path: "cat_Latn/test/*.arrow"
- config_name: gym_Latn
data_files:
- split: train
path: "gym_Latn/train/*.arrow"
- split: dev
path: "gym_Latn/dev/*.arrow"
- split: test
path: "gym_Latn/test/*.arrow"
- config_name: sat_Olck
data_files:
- split: train
path: "sat_Olck/train/*.arrow"
- split: dev
path: "sat_Olck/dev/*.arrow"
- split: test
path: "sat_Olck/test/*.arrow"
- config_name: snd_Arab
data_files:
- split: train
path: "snd_Arab/train/*.arrow"
- split: dev
path: "snd_Arab/dev/*.arrow"
- split: test
path: "snd_Arab/test/*.arrow"
- config_name: isl_Latn
data_files:
- split: train
path: "isl_Latn/train/*.arrow"
- split: dev
path: "isl_Latn/dev/*.arrow"
- split: test
path: "isl_Latn/test/*.arrow"
- config_name: mlt_Telu
data_files:
- split: train
path: "mlt_Telu/train/*.arrow"
- split: dev
path: "mlt_Telu/dev/*.arrow"
- split: test
path: "mlt_Telu/test/*.arrow"
- config_name: kal_Latn
data_files:
- split: train
path: "kal_Latn/train/*.arrow"
- split: dev
path: "kal_Latn/dev/*.arrow"
- split: test
path: "kal_Latn/test/*.arrow"
- config_name: aoj_Latn
data_files:
- split: train
path: "aoj_Latn/train/*.arrow"
- split: dev
path: "aoj_Latn/dev/*.arrow"
- split: test
path: "aoj_Latn/test/*.arrow"
- config_name: zai_Latn
data_files:
- split: train
path: "zai_Latn/train/*.arrow"
- split: dev
path: "zai_Latn/dev/*.arrow"
- split: test
path: "zai_Latn/test/*.arrow"
- config_name: guj_Gujr
data_files:
- split: train
path: "guj_Gujr/train/*.arrow"
- split: dev
path: "guj_Gujr/dev/*.arrow"
- split: test
path: "guj_Gujr/test/*.arrow"
- config_name: min_Latn
data_files:
- split: train
path: "min_Latn/train/*.arrow"
- split: dev
path: "min_Latn/dev/*.arrow"
- split: test
path: "min_Latn/test/*.arrow"
- config_name: grc_Grek
data_files:
- split: train
path: "grc_Grek/train/*.arrow"
- split: dev
path: "grc_Grek/dev/*.arrow"
- split: test
path: "grc_Grek/test/*.arrow"
- config_name: hmn_Latn
data_files:
- split: train
path: "hmn_Latn/train/*.arrow"
- split: dev
path: "hmn_Latn/dev/*.arrow"
- split: test
path: "hmn_Latn/test/*.arrow"
- config_name: ido_Latn
data_files:
- split: train
path: "ido_Latn/train/*.arrow"
- split: dev
path: "ido_Latn/dev/*.arrow"
- split: test
path: "ido_Latn/test/*.arrow"
- config_name: khm_Khmr
data_files:
- split: train
path: "khm_Khmr/train/*.arrow"
- split: dev
path: "khm_Khmr/dev/*.arrow"
- split: test
path: "khm_Khmr/test/*.arrow"
- config_name: quh_Latn
data_files:
- split: train
path: "quh_Latn/train/*.arrow"
- split: dev
path: "quh_Latn/dev/*.arrow"
- split: test
path: "quh_Latn/test/*.arrow"
- config_name: ikk_Latn
data_files:
- split: train
path: "ikk_Latn/train/*.arrow"
- split: dev
path: "ikk_Latn/dev/*.arrow"
- split: test
path: "ikk_Latn/test/*.arrow"
- config_name: iku_Cans
data_files:
- split: train
path: "iku_Cans/train/*.arrow"
- split: dev
path: "iku_Cans/dev/*.arrow"
- split: test
path: "iku_Cans/test/*.arrow"
- config_name: tat_Latn
data_files:
- split: train
path: "tat_Latn/train/*.arrow"
- split: dev
path: "tat_Latn/dev/*.arrow"
- split: test
path: "tat_Latn/test/*.arrow"
- config_name: bel_Cyrl
data_files:
- split: train
path: "bel_Cyrl/train/*.arrow"
- split: dev
path: "bel_Cyrl/dev/*.arrow"
- split: test
path: "bel_Cyrl/test/*.arrow"
- config_name: dyu_Latn
data_files:
- split: train
path: "dyu_Latn/train/*.arrow"
- split: dev
path: "dyu_Latn/dev/*.arrow"
- split: test
path: "dyu_Latn/test/*.arrow"
- config_name: guj_Thai
data_files:
- split: train
path: "guj_Thai/train/*.arrow"
- split: dev
path: "guj_Thai/dev/*.arrow"
- split: test
path: "guj_Thai/test/*.arrow"
- config_name: que_Latn
data_files:
- split: train
path: "que_Latn/train/*.arrow"
- split: dev
path: "que_Latn/dev/*.arrow"
- split: test
path: "que_Latn/test/*.arrow"
- config_name: wol_Latn
data_files:
- split: train
path: "wol_Latn/train/*.arrow"
- split: dev
path: "wol_Latn/dev/*.arrow"
- split: test
path: "wol_Latn/test/*.arrow"
- config_name: hne_Deva
data_files:
- split: train
path: "hne_Deva/train/*.arrow"
- split: dev
path: "hne_Deva/dev/*.arrow"
- split: test
path: "hne_Deva/test/*.arrow"
- config_name: zho_Hani
data_files:
- split: train
path: "zho_Hani/train/*.arrow"
- split: dev
path: "zho_Hani/dev/*.arrow"
- split: test
path: "zho_Hani/test/*.arrow"
- config_name: tum_Latn
data_files:
- split: train
path: "tum_Latn/train/*.arrow"
- split: dev
path: "tum_Latn/dev/*.arrow"
- split: test
path: "tum_Latn/test/*.arrow"
- config_name: swh_Latn
data_files:
- split: train
path: "swh_Latn/train/*.arrow"
- split: dev
path: "swh_Latn/dev/*.arrow"
- split: test
path: "swh_Latn/test/*.arrow"
- config_name: kua_Latn
data_files:
- split: train
path: "kua_Latn/train/*.arrow"
- config_name: ncj_Latn
data_files:
- split: train
path: "ncj_Latn/train/*.arrow"
- split: dev
path: "ncj_Latn/dev/*.arrow"
- split: test
path: "ncj_Latn/test/*.arrow"
- config_name: ewe_Latn
data_files:
- split: train
path: "ewe_Latn/train/*.arrow"
- split: dev
path: "ewe_Latn/dev/*.arrow"
- split: test
path: "ewe_Latn/test/*.arrow"
- config_name: mlt_Geor
data_files:
- split: train
path: "mlt_Geor/train/*.arrow"
- split: dev
path: "mlt_Geor/dev/*.arrow"
- split: test
path: "mlt_Geor/test/*.arrow"
- config_name: hat_Latn
data_files:
- split: train
path: "hat_Latn/train/*.arrow"
- split: dev
path: "hat_Latn/dev/*.arrow"
- split: test
path: "hat_Latn/test/*.arrow"
- config_name: guj_Hani
data_files:
- split: train
path: "guj_Hani/train/*.arrow"
- split: dev
path: "guj_Hani/dev/*.arrow"
- split: test
path: "guj_Hani/test/*.arrow"
- config_name: ina_Latn
data_files:
- split: train
path: "ina_Latn/train/*.arrow"
- split: dev
path: "ina_Latn/dev/*.arrow"
- split: test
path: "ina_Latn/test/*.arrow"
- config_name: ahk_Latn
data_files:
- split: train
path: "ahk_Latn/train/*.arrow"
- split: dev
path: "ahk_Latn/dev/*.arrow"
- split: test
path: "ahk_Latn/test/*.arrow"
- config_name: srm_Latn
data_files:
- split: train
path: "srm_Latn/train/*.arrow"
- split: dev
path: "srm_Latn/dev/*.arrow"
- split: test
path: "srm_Latn/test/*.arrow"
- config_name: lug_Latn
data_files:
- split: train
path: "lug_Latn/train/*.arrow"
- split: dev
path: "lug_Latn/dev/*.arrow"
- split: test
path: "lug_Latn/test/*.arrow"
- config_name: ach_Latn
data_files:
- split: train
path: "ach_Latn/train/*.arrow"
- config_name: rmy_Latn
data_files:
- split: train
path: "rmy_Latn/train/*.arrow"
- split: dev
path: "rmy_Latn/dev/*.arrow"
- split: test
path: "rmy_Latn/test/*.arrow"
- config_name: smo_Latn
data_files:
- split: train
path: "smo_Latn/train/*.arrow"
- split: dev
path: "smo_Latn/dev/*.arrow"
- split: test
path: "smo_Latn/test/*.arrow"
- config_name: mos_Latn
data_files:
- split: train
path: "mos_Latn/train/*.arrow"
- split: dev
path: "mos_Latn/dev/*.arrow"
- split: test
path: "mos_Latn/test/*.arrow"
- config_name: srd_Latn
data_files:
- split: train
path: "srd_Latn/train/*.arrow"
- split: dev
path: "srd_Latn/dev/*.arrow"
- split: test
path: "srd_Latn/test/*.arrow"
- config_name: srp_Latn
data_files:
- split: train
path: "srp_Latn/train/*.arrow"
- split: dev
path: "srp_Latn/dev/*.arrow"
- split: test
path: "srp_Latn/test/*.arrow"
- config_name: azb_Arab
data_files:
- split: train
path: "azb_Arab/train/*.arrow"
- split: dev
path: "azb_Arab/dev/*.arrow"
- split: test
path: "azb_Arab/test/*.arrow"
- config_name: aze_Arab
data_files:
- split: train
path: "aze_Arab/train/*.arrow"
- split: dev
path: "aze_Arab/dev/*.arrow"
- split: test
path: "aze_Arab/test/*.arrow"
- config_name: ori_Orya
data_files:
- split: train
path: "ori_Orya/train/*.arrow"
- split: dev
path: "ori_Orya/dev/*.arrow"
- split: test
path: "ori_Orya/test/*.arrow"
- config_name: mzh_Latn
data_files:
- split: train
path: "mzh_Latn/train/*.arrow"
- split: dev
path: "mzh_Latn/dev/*.arrow"
- split: test
path: "mzh_Latn/test/*.arrow"
- config_name: kur_Latn
data_files:
- split: train
path: "kur_Latn/train/*.arrow"
- split: dev
path: "kur_Latn/dev/*.arrow"
- split: test
path: "kur_Latn/test/*.arrow"
- config_name: wbm_Latn
data_files:
- split: train
path: "wbm_Latn/train/*.arrow"
- split: dev
path: "wbm_Latn/dev/*.arrow"
- config_name: crs_Latn
data_files:
- split: train
path: "crs_Latn/train/*.arrow"
- config_name: aze_Deva
data_files:
- split: train
path: "aze_Deva/train/*.arrow"
- split: dev
path: "aze_Deva/dev/*.arrow"
- split: test
path: "aze_Deva/test/*.arrow"
- config_name: tsn_Arab
data_files:
- split: train
path: "tsn_Arab/train/*.arrow"
- split: dev
path: "tsn_Arab/dev/*.arrow"
- split: test
path: "tsn_Arab/test/*.arrow"
- config_name: ada_Latn
data_files:
- split: train
path: "ada_Latn/train/*.arrow"
- config_name: hif_Latn
data_files:
- split: train
path: "hif_Latn/train/*.arrow"
- split: dev
path: "hif_Latn/dev/*.arrow"
- split: test
path: "hif_Latn/test/*.arrow"
- config_name: guj_Grek
data_files:
- split: train
path: "guj_Grek/train/*.arrow"
- split: dev
path: "guj_Grek/dev/*.arrow"
- split: test
path: "guj_Grek/test/*.arrow"
- config_name: pcm_Latn
data_files:
- split: train
path: "pcm_Latn/train/*.arrow"
- split: dev
path: "pcm_Latn/dev/*.arrow"
- split: test
path: "pcm_Latn/test/*.arrow"
- config_name: tso_Latn
data_files:
- split: train
path: "tso_Latn/train/*.arrow"
- split: dev
path: "tso_Latn/dev/*.arrow"
- split: test
path: "tso_Latn/test/*.arrow"
- config_name: nor_Latn
data_files:
- split: train
path: "nor_Latn/train/*.arrow"
- split: dev
path: "nor_Latn/dev/*.arrow"
- split: test
path: "nor_Latn/test/*.arrow"
- config_name: bsb_Latn
data_files:
- split: train
path: "bsb_Latn/train/*.arrow"
- split: dev
path: "bsb_Latn/dev/*.arrow"
- split: test
path: "bsb_Latn/test/*.arrow"
- config_name: uig_Cyrl
data_files:
- split: train
path: "uig_Cyrl/train/*.arrow"
- split: dev
path: "uig_Cyrl/dev/*.arrow"
- split: test
path: "uig_Cyrl/test/*.arrow"
- config_name: gaa_Latn
data_files:
- split: train
path: "gaa_Latn/train/*.arrow"
- config_name: ukr_Cyrl
data_files:
- split: train
path: "ukr_Cyrl/train/*.arrow"
- split: dev
path: "ukr_Cyrl/dev/*.arrow"
- split: test
path: "ukr_Cyrl/test/*.arrow"
- config_name: lav_Latn
data_files:
- split: train
path: "lav_Latn/train/*.arrow"
- split: dev
path: "lav_Latn/dev/*.arrow"
- split: test
path: "lav_Latn/test/*.arrow"
- config_name: mon_Latn
data_files:
- split: train
path: "mon_Latn/train/*.arrow"
- split: dev
path: "mon_Latn/dev/*.arrow"
- split: test
path: "mon_Latn/test/*.arrow"
- config_name: nep_Deva
data_files:
- split: train
path: "nep_Deva/train/*.arrow"
- split: dev
path: "nep_Deva/dev/*.arrow"
- split: test
path: "nep_Deva/test/*.arrow"
- config_name: aze_Telu
data_files:
- split: train
path: "aze_Telu/train/*.arrow"
- split: dev
path: "aze_Telu/dev/*.arrow"
- split: test
path: "aze_Telu/test/*.arrow"
- config_name: guj_Deva
data_files:
- split: train
path: "guj_Deva/train/*.arrow"
- split: dev
path: "guj_Deva/dev/*.arrow"
- split: test
path: "guj_Deva/test/*.arrow"
- config_name: pis_Latn
data_files:
- split: train
path: "pis_Latn/train/*.arrow"
- config_name: lhu_Latn
data_files:
- split: train
path: "lhu_Latn/train/*.arrow"
- split: dev
path: "lhu_Latn/dev/*.arrow"
- split: test
path: "lhu_Latn/test/*.arrow"
- config_name: bew_Latn
data_files:
- split: train
path: "bew_Latn/train/*.arrow"
- split: dev
path: "bew_Latn/dev/*.arrow"
- split: test
path: "bew_Latn/test/*.arrow"
- config_name: nya_Latn
data_files:
- split: train
path: "nya_Latn/train/*.arrow"
- split: dev
path: "nya_Latn/dev/*.arrow"
- split: test
path: "nya_Latn/test/*.arrow"
- config_name: poh_Latn
data_files:
- split: train
path: "poh_Latn/train/*.arrow"
- split: dev
path: "poh_Latn/dev/*.arrow"
- split: test
path: "poh_Latn/test/*.arrow"
- config_name: nnb_Latn
data_files:
- split: train
path: "nnb_Latn/train/*.arrow"
- split: dev
path: "nnb_Latn/dev/*.arrow"
- split: test
path: "nnb_Latn/test/*.arrow"
- config_name: grn_Latn
data_files:
- split: train
path: "grn_Latn/train/*.arrow"
- split: dev
path: "grn_Latn/dev/*.arrow"
- split: test
path: "grn_Latn/test/*.arrow"
- config_name: mco_Latn
data_files:
- split: train
path: "mco_Latn/train/*.arrow"
- split: dev
path: "mco_Latn/dev/*.arrow"
- split: test
path: "mco_Latn/test/*.arrow"
- config_name: ory_Orya
data_files:
- split: train
path: "ory_Orya/train/*.arrow"
- split: dev
path: "ory_Orya/dev/*.arrow"
- split: test
path: "ory_Orya/test/*.arrow"
- config_name: ful_Latn
data_files:
- split: train
path: "ful_Latn/train/*.arrow"
- split: dev
path: "ful_Latn/dev/*.arrow"
- split: test
path: "ful_Latn/test/*.arrow"
- config_name: diq_Latn
data_files:
- split: train
path: "diq_Latn/train/*.arrow"
- split: dev
path: "diq_Latn/dev/*.arrow"
- split: test
path: "diq_Latn/test/*.arrow"
- config_name: sag_Latn
data_files:
- split: train
path: "sag_Latn/train/*.arrow"
- split: dev
path: "sag_Latn/dev/*.arrow"
- split: test
path: "sag_Latn/test/*.arrow"
- config_name: tel_Telu
data_files:
- split: train
path: "tel_Telu/train/*.arrow"
- split: dev
path: "tel_Telu/dev/*.arrow"
- split: test
path: "tel_Telu/test/*.arrow"
- config_name: afr_Latn
data_files:
- split: train
path: "afr_Latn/train/*.arrow"
- split: dev
path: "afr_Latn/dev/*.arrow"
- split: test
path: "afr_Latn/test/*.arrow"
- config_name: haw_Latn
data_files:
- split: train
path: "haw_Latn/train/*.arrow"
- split: dev
path: "haw_Latn/dev/*.arrow"
- split: test
path: "haw_Latn/test/*.arrow"
- config_name: bar_Arab
data_files:
- split: train
path: "bar_Arab/train/*.arrow"
- split: dev
path: "bar_Arab/dev/*.arrow"
- split: test
path: "bar_Arab/test/*.arrow"
- config_name: umb_Latn
data_files:
- split: train
path: "umb_Latn/train/*.arrow"
- split: dev
path: "umb_Latn/dev/*.arrow"
- split: test
path: "umb_Latn/test/*.arrow"
- config_name: hsb_Latn
data_files:
- split: train
path: "hsb_Latn/train/*.arrow"
- split: dev
path: "hsb_Latn/dev/*.arrow"
- split: test
path: "hsb_Latn/test/*.arrow"
- config_name: fij_Latn
data_files:
- split: train
path: "fij_Latn/train/*.arrow"
- split: dev
path: "fij_Latn/dev/*.arrow"
- split: test
path: "fij_Latn/test/*.arrow"
- config_name: hbs_Cyrl
data_files:
- split: train
path: "hbs_Cyrl/train/*.arrow"
- split: dev
path: "hbs_Cyrl/dev/*.arrow"
- split: test
path: "hbs_Cyrl/test/*.arrow"
- config_name: san_Latn
data_files:
- split: train
path: "san_Latn/train/*.arrow"
- split: dev
path: "san_Latn/dev/*.arrow"
- split: test
path: "san_Latn/test/*.arrow"
- config_name: vls_Latn
data_files:
- split: train
path: "vls_Latn/train/*.arrow"
- split: dev
path: "vls_Latn/dev/*.arrow"
- split: test
path: "vls_Latn/test/*.arrow"
- config_name: zsm_Latn
data_files:
- split: train
path: "zsm_Latn/train/*.arrow"
- split: dev
path: "zsm_Latn/dev/*.arrow"
- split: test
path: "zsm_Latn/test/*.arrow"
- config_name: lij_Latn
data_files:
- split: train
path: "lij_Latn/train/*.arrow"
- split: dev
path: "lij_Latn/dev/*.arrow"
- split: test
path: "lij_Latn/test/*.arrow"
- config_name: quc_Latn
data_files:
- split: train
path: "quc_Latn/train/*.arrow"
- split: dev
path: "quc_Latn/dev/*.arrow"
- split: test
path: "quc_Latn/test/*.arrow"
- config_name: mam_Latn
data_files:
- split: train
path: "mam_Latn/train/*.arrow"
- split: dev
path: "mam_Latn/dev/*.arrow"
- split: test
path: "mam_Latn/test/*.arrow"
- config_name: tls_Latn
data_files:
- split: train
path: "tls_Latn/train/*.arrow"
- split: dev
path: "tls_Latn/dev/*.arrow"
- split: test
path: "tls_Latn/test/*.arrow"
- config_name: tuc_Latn
data_files:
- split: train
path: "tuc_Latn/train/*.arrow"
- split: dev
path: "tuc_Latn/dev/*.arrow"
- split: test
path: "tuc_Latn/test/*.arrow"
- config_name: dan_Latn
data_files:
- split: train
path: "dan_Latn/train/*.arrow"
- split: dev
path: "dan_Latn/dev/*.arrow"
- split: test
path: "dan_Latn/test/*.arrow"
- config_name: rue_Cyrl
data_files:
- split: train
path: "rue_Cyrl/train/*.arrow"
- split: dev
path: "rue_Cyrl/dev/*.arrow"
- split: test
path: "rue_Cyrl/test/*.arrow"
- config_name: mlt_Guru
data_files:
- split: train
path: "mlt_Guru/train/*.arrow"
- split: dev
path: "mlt_Guru/dev/*.arrow"
- split: test
path: "mlt_Guru/test/*.arrow"
- config_name: ace_Latn
data_files:
- split: train
path: "ace_Latn/train/*.arrow"
- split: dev
path: "ace_Latn/dev/*.arrow"
- split: test
path: "ace_Latn/test/*.arrow"
- config_name: bem_Latn
data_files:
- split: train
path: "bem_Latn/train/*.arrow"
- split: dev
path: "bem_Latn/dev/*.arrow"
- split: test
path: "bem_Latn/test/*.arrow"
- config_name: kam_Latn
data_files:
- split: train
path: "kam_Latn/train/*.arrow"
- split: dev
path: "kam_Latn/dev/*.arrow"
- split: test
path: "kam_Latn/test/*.arrow"
- config_name: uig_Hani
data_files:
- split: train
path: "uig_Hani/train/*.arrow"
- split: dev
path: "uig_Hani/dev/*.arrow"
- split: test
path: "uig_Hani/test/*.arrow"
- config_name: kaa_Latn
data_files:
- split: train
path: "kaa_Latn/train/*.arrow"
- split: dev
path: "kaa_Latn/dev/*.arrow"
- split: test
path: "kaa_Latn/test/*.arrow"
- config_name: ndo_Latn
data_files:
- split: train
path: "ndo_Latn/train/*.arrow"
- split: dev
path: "ndo_Latn/dev/*.arrow"
- split: test
path: "ndo_Latn/test/*.arrow"
- config_name: aze_Knda
data_files:
- split: train
path: "aze_Knda/train/*.arrow"
- split: dev
path: "aze_Knda/dev/*.arrow"
- split: test
path: "aze_Knda/test/*.arrow"
- config_name: oss_Cyrl
data_files:
- split: train
path: "oss_Cyrl/train/*.arrow"
- split: dev
path: "oss_Cyrl/dev/*.arrow"
- split: test
path: "oss_Cyrl/test/*.arrow"
- config_name: lit_Latn
data_files:
- split: train
path: "lit_Latn/train/*.arrow"
- split: dev
path: "lit_Latn/dev/*.arrow"
- split: test
path: "lit_Latn/test/*.arrow"
- config_name: frr_Latn
data_files:
- split: train
path: "frr_Latn/train/*.arrow"
- split: dev
path: "frr_Latn/dev/*.arrow"
- split: test
path: "frr_Latn/test/*.arrow"
- config_name: yap_Latn
data_files:
- split: train
path: "yap_Latn/train/*.arrow"
- split: dev
path: "yap_Latn/dev/*.arrow"
- split: test
path: "yap_Latn/test/*.arrow"
- config_name: gom_Latn
data_files:
- split: train
path: "gom_Latn/train/*.arrow"
- split: dev
path: "gom_Latn/dev/*.arrow"
- split: test
path: "gom_Latn/test/*.arrow"
- config_name: swe_Latn
data_files:
- split: train
path: "swe_Latn/train/*.arrow"
- split: dev
path: "swe_Latn/dev/*.arrow"
- split: test
path: "swe_Latn/test/*.arrow"
- config_name: lfn_Latn
data_files:
- split: train
path: "lfn_Latn/train/*.arrow"
- split: dev
path: "lfn_Latn/dev/*.arrow"
- split: test
path: "lfn_Latn/test/*.arrow"
- config_name: cmn_Hani
data_files:
- split: train
path: "cmn_Hani/train/*.arrow"
- split: dev
path: "cmn_Hani/dev/*.arrow"
- split: test
path: "cmn_Hani/test/*.arrow"
- config_name: mon_Cyrl
data_files:
- split: train
path: "mon_Cyrl/train/*.arrow"
- split: dev
path: "mon_Cyrl/dev/*.arrow"
- split: test
path: "mon_Cyrl/test/*.arrow"
- config_name: vep_Latn
data_files:
- split: train
path: "vep_Latn/train/*.arrow"
- split: dev
path: "vep_Latn/dev/*.arrow"
- split: test
path: "vep_Latn/test/*.arrow"
- config_name: ixl_Latn
data_files:
- split: train
path: "ixl_Latn/train/*.arrow"
- split: dev
path: "ixl_Latn/dev/*.arrow"
- split: test
path: "ixl_Latn/test/*.arrow"
- config_name: mlt_Gujr
data_files:
- split: train
path: "mlt_Gujr/train/*.arrow"
- split: dev
path: "mlt_Gujr/dev/*.arrow"
- split: test
path: "mlt_Gujr/test/*.arrow"
- config_name: gil_Latn
data_files:
- split: train
path: "gil_Latn/train/*.arrow"
- config_name: mau_Latn
data_files:
- split: train
path: "mau_Latn/train/*.arrow"
- split: dev
path: "mau_Latn/dev/*.arrow"
- split: test
path: "mau_Latn/test/*.arrow"
- config_name: tsn_Latn
data_files:
- split: train
path: "tsn_Latn/train/*.arrow"
- split: dev
path: "tsn_Latn/dev/*.arrow"
- split: test
path: "tsn_Latn/test/*.arrow"
- config_name: aym_Latn
data_files:
- split: train
path: "aym_Latn/train/*.arrow"
- split: dev
path: "aym_Latn/dev/*.arrow"
- split: test
path: "aym_Latn/test/*.arrow"
- config_name: vec_Latn
data_files:
- split: train
path: "vec_Latn/train/*.arrow"
- split: dev
path: "vec_Latn/dev/*.arrow"
- split: test
path: "vec_Latn/test/*.arrow"
- config_name: gom_Deva
data_files:
- split: train
path: "gom_Deva/train/*.arrow"
- split: dev
path: "gom_Deva/dev/*.arrow"
- split: test
path: "gom_Deva/test/*.arrow"
- config_name: fur_Latn
data_files:
- split: train
path: "fur_Latn/train/*.arrow"
- split: dev
path: "fur_Latn/dev/*.arrow"
- split: test
path: "fur_Latn/test/*.arrow"
- config_name: kin_Latn
data_files:
- split: train
path: "kin_Latn/train/*.arrow"
- split: dev
path: "kin_Latn/dev/*.arrow"
- split: test
path: "kin_Latn/test/*.arrow"
- config_name: guj_Hang
data_files:
- split: train
path: "guj_Hang/train/*.arrow"
- split: dev
path: "guj_Hang/dev/*.arrow"
- split: test
path: "guj_Hang/test/*.arrow"
- config_name: gcr_Latn
data_files:
- split: train
path: "gcr_Latn/train/*.arrow"
- config_name: sgs_Latn
data_files:
- split: train
path: "sgs_Latn/train/*.arrow"
- split: dev
path: "sgs_Latn/dev/*.arrow"
- split: test
path: "sgs_Latn/test/*.arrow"
- config_name: bih_Deva
data_files:
- split: train
path: "bih_Deva/train/*.arrow"
- split: dev
path: "bih_Deva/dev/*.arrow"
- split: test
path: "bih_Deva/test/*.arrow"
- config_name: guj_Guru
data_files:
- split: train
path: "guj_Guru/train/*.arrow"
- split: dev
path: "guj_Guru/dev/*.arrow"
- split: test
path: "guj_Guru/test/*.arrow"
- config_name: vie_Latn
data_files:
- split: train
path: "vie_Latn/train/*.arrow"
- split: dev
path: "vie_Latn/dev/*.arrow"
- split: test
path: "vie_Latn/test/*.arrow"
- config_name: tha_Thai
data_files:
- split: train
path: "tha_Thai/train/*.arrow"
- split: dev
path: "tha_Thai/dev/*.arrow"
- split: test
path: "tha_Thai/test/*.arrow"
- config_name: pau_Latn
data_files:
- split: train
path: "pau_Latn/train/*.arrow"
- config_name: est_Latn
data_files:
- split: train
path: "est_Latn/train/*.arrow"
- split: dev
path: "est_Latn/dev/*.arrow"
- split: test
path: "est_Latn/test/*.arrow"
- config_name: lue_Latn
data_files:
- split: train
path: "lue_Latn/train/*.arrow"
- config_name: rug_Latn
data_files:
- split: train
path: "rug_Latn/train/*.arrow"
- split: dev
path: "rug_Latn/dev/*.arrow"
- split: test
path: "rug_Latn/test/*.arrow"
- config_name: kjb_Latn
data_files:
- split: train
path: "kjb_Latn/train/*.arrow"
- split: dev
path: "kjb_Latn/dev/*.arrow"
- split: test
path: "kjb_Latn/test/*.arrow"
- config_name: kik_Latn
data_files:
- split: train
path: "kik_Latn/train/*.arrow"
- split: dev
path: "kik_Latn/dev/*.arrow"
- split: test
path: "kik_Latn/test/*.arrow"
- config_name: mri_Latn
data_files:
- split: train
path: "mri_Latn/train/*.arrow"
- split: dev
path: "mri_Latn/dev/*.arrow"
- split: test
path: "mri_Latn/test/*.arrow"
- config_name: ber_Latn
data_files:
- split: train
path: "ber_Latn/train/*.arrow"
- split: dev
path: "ber_Latn/dev/*.arrow"
- split: test
path: "ber_Latn/test/*.arrow"
- config_name: ssw_Latn
data_files:
- split: train
path: "ssw_Latn/train/*.arrow"
- split: dev
path: "ssw_Latn/dev/*.arrow"
- split: test
path: "ssw_Latn/test/*.arrow"
- config_name: guj_Beng
data_files:
- split: train
path: "guj_Beng/train/*.arrow"
- split: dev
path: "guj_Beng/dev/*.arrow"
- split: test
path: "guj_Beng/test/*.arrow"
- config_name: quz_Latn
data_files:
- split: train
path: "quz_Latn/train/*.arrow"
- config_name: arb_Arab
data_files:
- split: train
path: "arb_Arab/train/*.arrow"
- split: dev
path: "arb_Arab/dev/*.arrow"
- split: test
path: "arb_Arab/test/*.arrow"
- config_name: mlt_Sinh
data_files:
- split: train
path: "mlt_Sinh/train/*.arrow"
- split: dev
path: "mlt_Sinh/dev/*.arrow"
- split: test
path: "mlt_Sinh/test/*.arrow"
- config_name: mai_Deva
data_files:
- split: train
path: "mai_Deva/train/*.arrow"
- split: dev
path: "mai_Deva/dev/*.arrow"
- split: test
path: "mai_Deva/test/*.arrow"
- config_name: mlt_Thai
data_files:
- split: train
path: "mlt_Thai/train/*.arrow"
- split: dev
path: "mlt_Thai/dev/*.arrow"
- split: test
path: "mlt_Thai/test/*.arrow"
- config_name: bew_Cyrl
data_files:
- split: train
path: "bew_Cyrl/train/*.arrow"
- split: dev
path: "bew_Cyrl/dev/*.arrow"
- split: test
path: "bew_Cyrl/test/*.arrow"
- config_name: tat_Cyrl
data_files:
- split: train
path: "tat_Cyrl/train/*.arrow"
- split: dev
path: "tat_Cyrl/dev/*.arrow"
- split: test
path: "tat_Cyrl/test/*.arrow"
- config_name: mya_Mymr
data_files:
- split: train
path: "mya_Mymr/train/*.arrow"
- split: dev
path: "mya_Mymr/dev/*.arrow"
- split: test
path: "mya_Mymr/test/*.arrow"
- config_name: alt_Cyrl
data_files:
- split: train
path: "alt_Cyrl/train/*.arrow"
- split: dev
path: "alt_Cyrl/dev/*.arrow"
- split: test
path: "alt_Cyrl/test/*.arrow"
- config_name: nno_Latn
data_files:
- split: train
path: "nno_Latn/train/*.arrow"
- split: dev
path: "nno_Latn/dev/*.arrow"
- split: test
path: "nno_Latn/test/*.arrow"
- config_name: hrx_Latn
data_files:
- split: train
path: "hrx_Latn/train/*.arrow"
- split: dev
path: "hrx_Latn/dev/*.arrow"
- split: test
path: "hrx_Latn/test/*.arrow"
- config_name: hau_Latn
data_files:
- split: train
path: "hau_Latn/train/*.arrow"
- split: dev
path: "hau_Latn/dev/*.arrow"
- split: test
path: "hau_Latn/test/*.arrow"
- config_name: gsw_Latn
data_files:
- split: train
path: "gsw_Latn/train/*.arrow"
- split: dev
path: "gsw_Latn/dev/*.arrow"
- split: test
path: "gsw_Latn/test/*.arrow"
- config_name: pam_Latn
data_files:
- split: train
path: "pam_Latn/train/*.arrow"
- split: dev
path: "pam_Latn/dev/*.arrow"
- split: test
path: "pam_Latn/test/*.arrow"
- config_name: mlt_Deva
data_files:
- split: train
path: "mlt_Deva/train/*.arrow"
- split: dev
path: "mlt_Deva/dev/*.arrow"
- split: test
path: "mlt_Deva/test/*.arrow"
- config_name: sun_Latn
data_files:
- split: train
path: "sun_Latn/train/*.arrow"
- split: dev
path: "sun_Latn/dev/*.arrow"
- split: test
path: "sun_Latn/test/*.arrow"
- config_name: aze_Sinh
data_files:
- split: train
path: "aze_Sinh/train/*.arrow"
- split: dev
path: "aze_Sinh/dev/*.arrow"
- split: test
path: "aze_Sinh/test/*.arrow"
- config_name: lat_Latn
data_files:
- split: train
path: "lat_Latn/train/*.arrow"
- split: dev
path: "lat_Latn/dev/*.arrow"
- split: test
path: "lat_Latn/test/*.arrow"
- config_name: bis_Latn
data_files:
- split: train
path: "bis_Latn/train/*.arrow"
- split: dev
path: "bis_Latn/dev/*.arrow"
- split: test
path: "bis_Latn/test/*.arrow"
- config_name: udm_Cyrl
data_files:
- split: train
path: "udm_Cyrl/train/*.arrow"
- split: dev
path: "udm_Cyrl/dev/*.arrow"
- split: test
path: "udm_Cyrl/test/*.arrow"
- config_name: tca_Latn
data_files:
- split: train
path: "tca_Latn/train/*.arrow"
- split: dev
path: "tca_Latn/dev/*.arrow"
- split: test
path: "tca_Latn/test/*.arrow"
- config_name: uig_Arab
data_files:
- split: train
path: "uig_Arab/train/*.arrow"
- split: dev
path: "uig_Arab/dev/*.arrow"
- split: test
path: "uig_Arab/test/*.arrow"
- config_name: glg_Latn
data_files:
- split: train
path: "glg_Latn/train/*.arrow"
- split: dev
path: "glg_Latn/dev/*.arrow"
- split: test
path: "glg_Latn/test/*.arrow"
- config_name: tah_Latn
data_files:
- split: train
path: "tah_Latn/train/*.arrow"
- split: dev
path: "tah_Latn/dev/*.arrow"
- config_name: glk_Latn
data_files:
- split: train
path: "glk_Latn/train/*.arrow"
- split: dev
path: "glk_Latn/dev/*.arrow"
- split: test
path: "glk_Latn/test/*.arrow"
- config_name: aze_Tfng
data_files:
- split: train
path: "aze_Tfng/train/*.arrow"
- split: dev
path: "aze_Tfng/dev/*.arrow"
- split: test
path: "aze_Tfng/test/*.arrow"
- config_name: ckb_Arab
data_files:
- split: train
path: "ckb_Arab/train/*.arrow"
- split: dev
path: "ckb_Arab/dev/*.arrow"
- split: test
path: "ckb_Arab/test/*.arrow"
- config_name: gle_Latn
data_files:
- split: train
path: "gle_Latn/train/*.arrow"
- split: dev
path: "gle_Latn/dev/*.arrow"
- split: test
path: "gle_Latn/test/*.arrow"
- config_name: lim_Latn
data_files:
- split: train
path: "lim_Latn/train/*.arrow"
- split: dev
path: "lim_Latn/dev/*.arrow"
- split: test
path: "lim_Latn/test/*.arrow"
- config_name: slk_Latn
data_files:
- split: train
path: "slk_Latn/train/*.arrow"
- split: dev
path: "slk_Latn/dev/*.arrow"
- split: test
path: "slk_Latn/test/*.arrow"
- config_name: nds_Latn
data_files:
- split: train
path: "nds_Latn/train/*.arrow"
- split: dev
path: "nds_Latn/dev/*.arrow"
- split: test
path: "nds_Latn/test/*.arrow"
- config_name: kor_Hang
data_files:
- split: train
path: "kor_Hang/train/*.arrow"
- split: dev
path: "kor_Hang/dev/*.arrow"
- split: test
path: "kor_Hang/test/*.arrow"
- config_name: uzb_Latn
data_files:
- split: train
path: "uzb_Latn/train/*.arrow"
- split: dev
path: "uzb_Latn/dev/*.arrow"
- split: test
path: "uzb_Latn/test/*.arrow"
- config_name: pfl_Latn
data_files:
- split: train
path: "pfl_Latn/train/*.arrow"
- split: dev
path: "pfl_Latn/dev/*.arrow"
- split: test
path: "pfl_Latn/test/*.arrow"
- config_name: azj_Latn
data_files:
- split: train
path: "azj_Latn/train/*.arrow"
- split: dev
path: "azj_Latn/dev/*.arrow"
- split: test
path: "azj_Latn/test/*.arrow"
- config_name: glv_Latn
data_files:
- split: train
path: "glv_Latn/train/*.arrow"
- split: dev
path: "glv_Latn/dev/*.arrow"
- split: test
path: "glv_Latn/test/*.arrow"
- config_name: jam_Latn
data_files:
- split: train
path: "jam_Latn/train/*.arrow"
- split: dev
path: "jam_Latn/dev/*.arrow"
- split: test
path: "jam_Latn/test/*.arrow"
- config_name: kat_Geor
data_files:
- split: train
path: "kat_Geor/train/*.arrow"
- split: dev
path: "kat_Geor/dev/*.arrow"
- split: test
path: "kat_Geor/test/*.arrow"
- config_name: fry_Latn
data_files:
- split: train
path: "fry_Latn/train/*.arrow"
- split: dev
path: "fry_Latn/dev/*.arrow"
- split: test
path: "fry_Latn/test/*.arrow"
- config_name: guj_Knda
data_files:
- split: train
path: "guj_Knda/train/*.arrow"
- split: dev
path: "guj_Knda/dev/*.arrow"
- split: test
path: "guj_Knda/test/*.arrow"
- config_name: kat_Latn
data_files:
- split: train
path: "kat_Latn/train/*.arrow"
- split: dev
path: "kat_Latn/dev/*.arrow"
- split: test
path: "kat_Latn/test/*.arrow"
- config_name: twi_Latn
data_files:
- split: train
path: "twi_Latn/train/*.arrow"
- split: dev
path: "twi_Latn/dev/*.arrow"
- split: test
path: "twi_Latn/test/*.arrow"
- config_name: eus_Latn
data_files:
- split: train
path: "eus_Latn/train/*.arrow"
- split: dev
path: "eus_Latn/dev/*.arrow"
- split: test
path: "eus_Latn/test/*.arrow"
- config_name: toi_Latn
data_files:
- split: train
path: "toi_Latn/train/*.arrow"
- config_name: mlt_Armn
data_files:
- split: train
path: "mlt_Armn/train/*.arrow"
- split: dev
path: "mlt_Armn/dev/*.arrow"
- split: test
path: "mlt_Armn/test/*.arrow"
- config_name: mon_Hira
data_files:
- split: train
path: "mon_Hira/train/*.arrow"
- split: dev
path: "mon_Hira/dev/*.arrow"
- split: test
path: "mon_Hira/test/*.arrow"
- config_name: mlg_Latn
data_files:
- split: train
path: "mlg_Latn/train/*.arrow"
- split: dev
path: "mlg_Latn/dev/*.arrow"
- split: test
path: "mlg_Latn/test/*.arrow"
- config_name: tyv_Cyrl
data_files:
- split: train
path: "tyv_Cyrl/train/*.arrow"
- split: dev
path: "tyv_Cyrl/dev/*.arrow"
- split: test
path: "tyv_Cyrl/test/*.arrow"
- config_name: arz_Arab
data_files:
- split: train
path: "arz_Arab/train/*.arrow"
- split: dev
path: "arz_Arab/dev/*.arrow"
- split: test
path: "arz_Arab/test/*.arrow"
- config_name: hyw_Armn
data_files:
- split: train
path: "hyw_Armn/train/*.arrow"
- config_name: chk_Latn
data_files:
- split: train
path: "chk_Latn/train/*.arrow"
- split: dev
path: "chk_Latn/dev/*.arrow"
- split: test
path: "chk_Latn/test/*.arrow"
- config_name: vol_Latn
data_files:
- split: train
path: "vol_Latn/train/*.arrow"
- split: dev
path: "vol_Latn/dev/*.arrow"
- split: test
path: "vol_Latn/test/*.arrow"
- config_name: kek_Latn
data_files:
- split: train
path: "kek_Latn/train/*.arrow"
- split: dev
path: "kek_Latn/dev/*.arrow"
- split: test
path: "kek_Latn/test/*.arrow"
- config_name: teo_Latn
data_files:
- split: train
path: "teo_Latn/train/*.arrow"
- config_name: ell_Grek
data_files:
- split: train
path: "ell_Grek/train/*.arrow"
- split: dev
path: "ell_Grek/dev/*.arrow"
- split: test
path: "ell_Grek/test/*.arrow"
- config_name: kan_Knda
data_files:
- split: train
path: "kan_Knda/train/*.arrow"
- split: dev
path: "kan_Knda/dev/*.arrow"
- split: test
path: "kan_Knda/test/*.arrow"
- config_name: tpi_Latn
data_files:
- split: train
path: "tpi_Latn/train/*.arrow"
- split: dev
path: "tpi_Latn/dev/*.arrow"
- split: test
path: "tpi_Latn/test/*.arrow"
- config_name: rop_Latn
data_files:
- split: train
path: "rop_Latn/train/*.arrow"
- split: dev
path: "rop_Latn/dev/*.arrow"
- split: test
path: "rop_Latn/test/*.arrow"
- config_name: aze_Mlym
data_files:
- split: train
path: "aze_Mlym/train/*.arrow"
- split: dev
path: "aze_Mlym/dev/*.arrow"
- split: test
path: "aze_Mlym/test/*.arrow"
- config_name: lua_Latn
data_files:
- split: train
path: "lua_Latn/train/*.arrow"
- split: dev
path: "lua_Latn/dev/*.arrow"
- config_name: mad_Latn
data_files:
- split: train
path: "mad_Latn/train/*.arrow"
- split: dev
path: "mad_Latn/dev/*.arrow"
- split: test
path: "mad_Latn/test/*.arrow"
- config_name: top_Latn
data_files:
- split: train
path: "top_Latn/train/*.arrow"
- split: dev
path: "top_Latn/dev/*.arrow"
- split: test
path: "top_Latn/test/*.arrow"
- config_name: scn_Latn
data_files:
- split: train
path: "scn_Latn/train/*.arrow"
- split: dev
path: "scn_Latn/dev/*.arrow"
- split: test
path: "scn_Latn/test/*.arrow"
- config_name: aze_Thaa
data_files:
- split: train
path: "aze_Thaa/train/*.arrow"
- split: dev
path: "aze_Thaa/dev/*.arrow"
- split: test
path: "aze_Thaa/test/*.arrow"
- config_name: guj_Latn
data_files:
- split: train
path: "guj_Latn/train/*.arrow"
- split: dev
path: "guj_Latn/dev/*.arrow"
- split: test
path: "guj_Latn/test/*.arrow"
- config_name: ngl_Latn
data_files:
- split: train
path: "ngl_Latn/train/*.arrow"
- split: dev
path: "ngl_Latn/dev/*.arrow"
- config_name: mal_Mlym
data_files:
- split: train
path: "mal_Mlym/train/*.arrow"
- split: dev
path: "mal_Mlym/dev/*.arrow"
- split: test
path: "mal_Mlym/test/*.arrow"
- config_name: szl_Latn
data_files:
- split: train
path: "szl_Latn/train/*.arrow"
- split: dev
path: "szl_Latn/dev/*.arrow"
- split: test
path: "szl_Latn/test/*.arrow"
- config_name: orm_Latn
data_files:
- split: train
path: "orm_Latn/train/*.arrow"
- split: dev
path: "orm_Latn/dev/*.arrow"
- split: test
path: "orm_Latn/test/*.arrow"
- config_name: urd_Arab
data_files:
- split: train
path: "urd_Arab/train/*.arrow"
- split: dev
path: "urd_Arab/dev/*.arrow"
- split: test
path: "urd_Arab/test/*.arrow"
- config_name: cbk_Latn
data_files:
- split: train
path: "cbk_Latn/train/*.arrow"
- split: dev
path: "cbk_Latn/dev/*.arrow"
- split: test
path: "cbk_Latn/test/*.arrow"
multilinguality:
- multilingual
language:
- abk
- abn
- ace
- ach
- acm
- acr
- ada
- afb
- afr
- ahk
- ajg
- ajp
- aka
- als
- alt
- alz
- amh
- ami
- aoj
- apc
- ara
- arb
- arn
- ary
- arz
- asm
- ast
- aym
- azb
- aze
- azj
- bak
- bam
- ban
- bas
- bba
- bbc
- bci
- bcl
- bel
- bem
- ben
- ber
- bhw
- bih
- bik
- bim
- bin
- bis
- bjn
- bod
- bos
- bpy
- bqc
- bre
- bsb
- bts
- btx
- bum
- bzj
- cab
- cak
- cat
- cbk
- cce
- cfm
- cgg
- che
- chk
- chv
- chw
- cjk
- ckb
- cmn
- cnh
- cos
- crh
- crs
- csb
- ctd
- ctu
- cuk
- cym
- dan
- dhv
- diq
- div
- djk
- dln
- dtp
- dua
- dyu
- dzo
- efi
- ekk
- ell
- eml
- eng
- epo
- est
- eus
- ewe
- ext
- fao
- fas
- fat
- fij
- fin
- fon
- fra
- frr
- fry
- ful
- fur
- gaa
- gcf
- gcr
- gil
- gkn
- gkp
- gla
- gle
- glg
- glv
- gom
- gor
- grc
- grn
- gsw
- guc
- gug
- guj
- gur
- guw
- gym
- hat
- hau
- haw
- hbs
- heb
- her
- hif
- hil
- hin
- hmn
- hmo
- hne
- hrv
- hrx
- hsb
- hui
- hun
- hus
- hye
- hyw
- iba
- ibg
- ibo
- ido
- idu
- ifa
- ifb
- ige
- ikk
- iku
- ile
- ilo
- ina
- ind
- ish
- isl
- ita
- ixl
- izz
- jam
- jav
- jbo
- kaa
- kab
- kac
- kal
- kam
- kan
- kat
- kaz
- kbd
- kbp
- kea
- kek
- khm
- kik
- kin
- kir
- kjh
- kmb
- kmr
- kom
- kon
- koo
- kor
- kos
- kpg
- kqn
- krc
- kri
- ksd
- ksh
- kss
- ksw
- kua
- kur
- kwn
- lam
- lao
- lat
- lav
- ldi
- leh
- lfn
- lhu
- lij
- lim
- lin
- lit
- llb
- lmo
- loz
- lua
- lue
- lug
- lun
- luo
- lus
- lvs
- lzh
- mad
- mah
- mai
- mal
- mam
- mar
- mau
- mbb
- mck
- mco
- mdy
- meu
- mfe
- mgh
- mgr
- mhr
- min
- miq
- mkd
- mlg
- mny
- mon
- mos
- mps
- mri
- mrw
- msa
- mwl
- mwn
- mxv
- mya
- myv
- mzh
- mzn
- nan
- nap
- naq
- nav
- nba
- nbl
- nch
- ncj
- ncx
- ndc
- nde
- ndo
- nds
- nep
- new
- ngl
- ngu
- nia
- niu
- nld
- nnb
- nno
- nob
- nor
- nse
- nso
- nya
- nyk
- nyn
- nyu
- nyy
- nzi
- oci
- ogo
- oke
- ori
- orm
- ory
- oss
- ote
- pag
- pam
- pan
- pap
- pau
- pcd
- pcm
- pdt
- pes
- pfl
- phm
- pis
- pls
- plt
- pms
- pnb
- poh
- pon
- por
- prk
- pus
- pxm
- qub
- quc
- que
- qug
- quw
- quy
- quz
- qvi
- rap
- rar
- rmn
- rmy
- rng
- roh
- ron
- rue
- rug
- run
- rus
- sag
- sah
- san
- sat
- scn
- sco
- seh
- sgs
- sid
- sin
- skg
- slk
- slv
- sme
- smo
- sna
- snd
- som
- sop
- sot
- sqi
- srd
- srm
- srn
- srp
- ssw
- sun
- suz
- swa
- swe
- swh
- sxn
- szl
- tah
- tam
- tat
- tbz
- tca
- tcf
- tdt
- tdx
- tel
- teo
- tgl
- tha
- tih
- tir
- tiv
- tlh
- tob
- tog
- toh
- toi
- toj
- tok
- ton
- top
- tpi
- tpm
- tsc
- tso
- tsz
- ttj
- tuc
- tuk
- tur
- twi
- twx
- tyv
- tzh
- tzo
- udm
- uig
- ukr
- umb
- urd
- urh
- uzb
- uzn
- ven
- vep
- vie
- vls
- vmw
- vol
- wal
- wes
- wln
- wol
- wuu
- xav
- xho
- xmf
- xmv
- yao
- yap
- yid
- yom
- yor
- yua
- yue
- zai
- zea
- zho
- zlm
- zne
- zpa
- zsm
- zul
pretty_name: Glot500 Corpus
---
# Glot500 Corpus
A dataset of natural language data collected by putting together more than 150
existing mono-lingual and multilingual datasets together and crawling known multilingual websites.
The focus of this dataset is on 500 extremely low-resource languages.
(More Languages still to be uploaded here)
This dataset is used to train the [Glot500](https://huggingface.co/cis-lmu/glot500-base) model.
- **Homepage:** [homepage](https://github.com/cisnlp/Glot500)
- **Repository:** [github](https://github.com/cisnlp/Glot500)
- **Paper:** [acl](https://aclanthology.org/2023.acl-long.61/), [arxiv](https://arxiv.org/abs/2305.12182)
## Usage
Replace `nbl_Latn` with your specific language.
```python
from datasets import load_dataset
dataset = load_dataset('cis-lmu/Glot500', 'nbl_Latn', split='train')
print(dataset['train'][0]) # First row of nbl_Latn
```
<details>
<summary>Click to show supported languages:</summary>
```
ton_Latn
nld_Latn
tzo_Latn
leh_Latn
cuk_Latn
ibg_Latn
uzb_Cyrl
jav_Latn
rap_Latn
zpa_Latn
bak_Cyrl
por_Latn
quy_Latn
ast_Latn
cos_Latn
fon_Latn
sna_Latn
dzo_Tibt
nob_Latn
nch_Latn
ish_Latn
che_Cyrl
ext_Latn
ldi_Latn
dtp_Latn
yue_Hani
kbd_Cyrl
mar_Deva
ron_Latn
acr_Latn
afb_Arab
sqi_Latn
eng_Latn
ksd_Latn
rus_Cyrl
bcl_Latn
ksh_Latn
hin_Latn
myv_Cyrl
kjh_Cyrl
sah_Cyrl
gkp_Latn
naq_Latn
tdt_Latn
rmn_Cyrl
kac_Latn
cak_Latn
kir_Cyrl
mps_Latn
yid_Hebr
dhv_Latn
srn_Latn
div_Thaa
mkd_Cyrl
idu_Latn
bre_Latn
bas_Latn
ven_Latn
pxm_Latn
wuu_Hani
mwl_Latn
miq_Latn
kss_Latn
wes_Latn
slv_Latn
hrv_Latn
hmo_Latn
som_Latn
bod_Tibt
pls_Latn
ile_Latn
luo_Latn
pus_Arab
fao_Latn
fas_Arab
swa_Latn
ifb_Latn
ary_Arab
tbz_Latn
hus_Latn
ote_Latn
ilo_Latn
ctd_Latn
abk_Cyrl
bqc_Latn
hil_Latn
pon_Latn
zul_Latn
als_Latn
pes_Arab
bpy_Beng
bos_Latn
sot_Latn
lin_Latn
tuk_Cyrl
gla_Latn
wln_Latn
apc_Arab
hin_Deva
hye_Armn
tir_Ethi
pap_Latn
gcf_Latn
cjk_Latn
pcd_Latn
tur_Latn
kon_Latn
mwn_Latn
izz_Latn
xho_Latn
lam_Latn
guc_Latn
aka_Latn
kea_Latn
sme_Latn
fat_Latn
csb_Latn
bak_Latn
djk_Latn
xav_Latn
oci_Latn
acm_Arab
rmy_Cyrl
bim_Latn
mck_Latn
krc_Cyrl
cym_Latn
lus_Latn
ncx_Latn
ngu_Latn
yom_Latn
tam_Taml
ajp_Arab
epo_Latn
fra_Latn
ita_Latn
seh_Latn
sxn_Latn
pdt_Latn
hbs_Latn
uzn_Cyrl
bhw_Latn
ksw_Mymr
pms_Latn
zlm_Latn
ami_Latn
qub_Latn
twx_Latn
tsz_Latn
kaa_Cyrl
toj_Latn
toh_Latn
kos_Latn
ogo_Latn
kab_Latn
pan_Guru
nan_Latn
aze_Latn
prk_Latn
ara_Arab
meu_Latn
nba_Latn
lvs_Latn
nbl_Latn
loz_Latn
crh_Latn
bci_Latn
kbp_Latn
tgl_Latn
kmb_Latn
hun_Latn
nzi_Latn
yao_Latn
arn_Latn
hyw_Cyrl
vmw_Latn
jbo_Latn
mzn_Arab
lzh_Hani
heb_Hebr
cce_Latn
bjn_Latn
gug_Latn
yor_Latn
ban_Latn
tlh_Latn
chv_Cyrl
sin_Sinh
ind_Latn
dua_Latn
sid_Latn
amh_Ethi
zea_Latn
kpg_Latn
crh_Cyrl
nyu_Latn
dln_Latn
ibo_Latn
tih_Latn
msa_Latn
nap_Latn
mgr_Latn
bik_Latn
srp_Cyrl
lao_Laoo
guw_Latn
kom_Cyrl
sop_Latn
nde_Latn
hui_Latn
cfm_Latn
new_Deva
kur_Arab
sco_Latn
nyk_Latn
lun_Latn
suz_Deva
wal_Latn
asm_Beng
rar_Latn
san_Deva
kaz_Cyrl
tog_Latn
iba_Latn
tuk_Latn
nso_Latn
run_Latn
ctu_Latn
bam_Latn
fin_Latn
gor_Latn
kmr_Latn
ben_Beng
pag_Latn
niu_Latn
xmf_Geor
ekk_Latn
tsc_Latn
lmo_Latn
mhr_Cyrl
plt_Latn
qvi_Latn
roh_Latn
oke_Latn
mah_Latn
tok_Latn
mgh_Latn
eml_Latn
urh_Latn
pnb_Arab
yua_Latn
nav_Latn
zne_Latn
bin_Latn
cat_Latn
gym_Latn
sat_Olck
snd_Arab
isl_Latn
rmn_Grek
bba_Latn
kal_Latn
aoj_Latn
qug_Latn
zai_Latn
guj_Gujr
min_Latn
tob_Latn
grc_Grek
hmn_Latn
ido_Latn
khm_Khmr
ikk_Latn
iku_Cans
tat_Latn
bel_Cyrl
dyu_Latn
que_Latn
efi_Latn
quw_Latn
nyn_Latn
wol_Latn
hne_Deva
zho_Hani
swh_Latn
bum_Latn
kua_Latn
ncj_Latn
ewe_Latn
hat_Latn
ina_Latn
mfe_Latn
ahk_Latn
srm_Latn
lug_Latn
ach_Latn
rmy_Latn
tpm_Latn
smo_Latn
mos_Latn
srd_Latn
srp_Latn
azb_Arab
ori_Orya
mzh_Latn
kur_Latn
phm_Latn
kwn_Latn
crs_Latn
ada_Latn
ttj_Latn
hif_Latn
tzh_Latn
tdx_Latn
bbc_Latn
cnh_Latn
pcm_Latn
tso_Latn
nor_Latn
bsb_Latn
kqn_Latn
gaa_Latn
ukr_Cyrl
lav_Latn
nep_Deva
kmr_Cyrl
ige_Latn
pis_Latn
lhu_Latn
nya_Latn
tiv_Latn
mny_Latn
kri_Latn
nyy_Latn
poh_Latn
nnb_Latn
grn_Latn
mco_Latn
ory_Orya
ful_Latn
diq_Latn
sag_Latn
tel_Telu
afr_Latn
haw_Latn
umb_Latn
hsb_Latn
fij_Latn
hbs_Cyrl
san_Latn
vls_Latn
zsm_Latn
lij_Latn
quc_Latn
mam_Latn
tuc_Latn
dan_Latn
rue_Cyrl
ace_Latn
bem_Latn
kam_Latn
ndo_Latn
mbb_Latn
mrw_Latn
ajg_Latn
oss_Cyrl
her_Latn
lit_Latn
frr_Latn
yap_Latn
bzj_Latn
gom_Latn
swe_Latn
lfn_Latn
cmn_Hani
mon_Cyrl
vep_Latn
ixl_Latn
gil_Latn
mau_Latn
aym_Latn
gom_Deva
fur_Latn
cgg_Latn
chw_Latn
kin_Latn
alz_Latn
ndc_Latn
gcr_Latn
rmn_Latn
sgs_Latn
bih_Deva
skg_Latn
bts_Latn
vie_Latn
tha_Thai
tcf_Latn
pau_Latn
est_Latn
lue_Latn
rug_Latn
gur_Latn
kik_Latn
mri_Latn
ber_Latn
ssw_Latn
cab_Latn
quz_Latn
arb_Arab
mai_Deva
tat_Cyrl
mya_Mymr
alt_Cyrl
nno_Latn
nse_Latn
hrx_Latn
hau_Latn
koo_Latn
gsw_Latn
pam_Latn
sun_Latn
lat_Latn
bis_Latn
btx_Latn
udm_Cyrl
xmv_Latn
tca_Latn
uig_Arab
glg_Latn
tah_Latn
llb_Latn
ckb_Arab
gle_Latn
lim_Latn
slk_Latn
nds_Latn
kor_Hang
uzb_Latn
gkn_Latn
pfl_Latn
azj_Latn
glv_Latn
jam_Latn
kat_Geor
abn_Latn
fry_Latn
kat_Latn
twi_Latn
eus_Latn
toi_Latn
mlg_Latn
ifa_Latn
tyv_Cyrl
arz_Arab
chk_Latn
vol_Latn
kek_Latn
teo_Latn
ell_Grek
kan_Knda
rng_Latn
tpi_Latn
mdy_Ethi
lua_Latn
mad_Latn
top_Latn
scn_Latn
ngl_Latn
mal_Mlym
szl_Latn
orm_Latn
nia_Latn
urd_Arab
mxv_Latn
cbk_Latn
```
</details>
## License
We don't own any part of the data. The original source of each sentence of the data is indicated in dataset field.
To see the copyright license of the original datasets visit [here](https://github.com/cisnlp/Glot500#glot500-c).
We license the actual packaging, the metadata and the annotations of these data under the cc0-1.0.
If you are a website/dataset owner and do not want your data to be included in this corpra, please send us an email at [email protected].
## Ethical Considerations
**1. Biases:** The text corpus may reflect the perspectives, opinions, or demographics of its sources or creators. It is important for users to critically evaluate the text in context especially for news sources and social medias.
**2. Representativeness:** While we have aimed for diversity and inclusivity, the text corpus may not fully represent all native speakers. Users should be mindful of any potential underrepresentation.
**3. Ethics:** We acknowledge that the collection and use of text data can have ethical implications. We have strived to handle the data responsibly, but we encourage users to consider the broader ethical implications of their own research or applications.
## Citation
If you use any part of this code and data in your research, please cite it using the following BibTeX entry.
```
@inproceedings{imanigooghari-etal-2023-glot500,
title = "Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages",
author = {ImaniGooghari, Ayyoob and
Lin, Peiqin and
Kargaran, Amir Hossein and
Severini, Silvia and
Jalili Sabet, Masoud and
Kassner, Nora and
Ma, Chunlan and
Schmid, Helmut and
Martins, Andr{\'e} and
Yvon, Fran{\c{c}}ois and
Sch{\"u}tze, Hinrich},
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.61",
doi = "10.18653/v1/2023.acl-long.61",
pages = "1082--1117",
abstract = "The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, {``}help{''} from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should notlimit NLP to a small fraction of the world{'}s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at \url{https://github.com/cisnlp/Glot500}.",
}
``` | [
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Atsushi/fungi_indexed_mycological_papers_japanese | Atsushi | 2023-12-14T21:21:26Z | 165 | 0 | [
"annotations_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:22Z | ---
annotations_creators:
- other
language:
- ja
license:
- cc-by-4.0
multilinguality:
- monolingual
source_datasets:
- original
size_categories:
- 1K<n<10K
---
fungi_indexed_mycological_papers_japanese
大菌輪「論文3行まとめ」データセット
最終更新日:2023/12/15(R3-11244まで)
====
### Languages
Japanese
This dataset is available in Japanese only.
# 概要
Atsushi Nakajima(中島淳志)が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け(インデキシング)した情報を提供しています。
本データセットは、「論文3行まとめ」のコンテンツに含まれる各論文の3行抄録、タグ(索引)、掲載種一覧、比較種一覧をまとめたものです。
「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。
また、本データセットを可視化したWebアプリを[Observableで公開](https://tinyurl.com/2tvryz8u)しています。
## 関連データセット
「識別形質まとめ」
[Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese)
「Trait Circusデータセット」(統制形質)
[Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database)
## 各カラムの説明
* R3ID … 大菌輪「論文3行まとめ」のIDです。
* ja_title_provisional_translate(仮訳和文題名) … 作成者が翻訳したタイトルです。一部、日本語の原題があるものはそれをそのまま使用しています。
* original_title(原文題名)
* published_year(出版年)
* journal_title(雑誌名)
* source(文献リンク) … 各情報の 出典(文献)のURLです。
* daikinrin_url … 大菌輪「論文3行まとめ」のURLです。
* tags … 作成者が論文を全文読んだ上で独自に付与した索引です。カンマ+半角空白区切りです。形態形質、宿主/基質、実験器具/実験手法/試薬、地理的分布、生理/生化学などを幅広く索引しています。
* R3summary_1 … 3行抄録の「1行目」です。
* R3summary_2 … 3行抄録の「2行目」です。
* R3summary_3 … 3行抄録の「3行目」です。
* species_reported(報告種一覧) … 当該論文内で掲載された種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。記号の意味は以下の通りです。
* ★=新種(新亜種・新品種・新変種)
* ■= 新産種
* ▲=新組み合わせ
* ◆=新学名
* ●=新階級
* (無印)=その他
* species_compared(比較種一覧) … いずれかの報告種と論文中で何らかの比較がなされた種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。詳細は「識別形質まとめ」データセット([Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese))を参照してください。
* taxon_reported(分類群一覧) … 報告種に対応する上位分類群をまとめたものです。カンマ+半角空白区切りです。MycoBankの情報を基に付与していますが、最新でない可能性があります。 | [
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Atsushi/fungi_diagnostic_chars_comparison_japanese | Atsushi | 2023-12-14T21:19:50Z | 163 | 0 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- other
language:
- ja
license:
- cc-by-4.0
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
size_categories:
- 100K<n<1M
---
fungi_diagnostic_chars_comparison_japanese
大菌輪「識別形質まとめ」データセット
最終更新日:2023/12/15(R3-11244まで)
====
### Languages
Japanese
This dataset is available in Japanese only.
# 概要
Atsushi Nakajima(中島淳志)が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け(インデキシング)した情報を提供しています。
その一環として、ある菌と別の菌の「共通する」あるいは「異なる」識別形質 (diagnostic characters) に関する記述を人手で抽出しています。
本データセットは、抽出された識別形質の一覧に、「色/color」、「形状/shape」などのカテゴリを半自動的に付与して集積したものです。
「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。
## 関連データセット
「論文3行まとめ」
[Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese)
「Trait Circusデータセット」(統制形質)
[Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database)
## 各カラムの説明
* R3ID … 大菌輪「論文3行まとめ」のIDです。
* No … 各識別文を一意のIDで区別するために、各R3IDにおいてナンバリングしたものです。
* comparison_source … 比較元の分類群(学名)です。
* comparison_target … 比較先の分類群(学名)です。
* sentence … 識別文です。全て日本語です。
* label …半自動的に付与されたカテゴリです(人手で修正していますが、ダブルチェックは行っていないので誤分類もあると思います)。以下の25のカテゴリが存在します。
* サイズ/size
* 分子系統解析/molecular_phylogenetic_analysis
* 形状/shape
* 色/color
* 地理的分布/geographical_distribution
* 生息環境/habitat
* 表面性状/surface_characteristics
* 構造/structure
* 有無/presence
* 形態全般/general_morphology
* 位置/position
* 二次代謝産物/secondary_metabolite
* 呈色反応/chemical_reaction
* 数量/amount
* 発達/development
* 生理学的形質/physiological_characters
* 分類/classification
* 資化・発酵能/assimilation_and_fermentation
* 質感/texture
* 味・臭い/taste_and_smell
* 病害・病原性関連/disease_and_pathogenecity
* 全般/general_characters
* 耐性・感受性/resistance_and_susceptibility
* 栄養摂取様式/nutrition_style
* 未分類/unclassified
* common_or_different … 共通する形質は「1」、異なる形質は「0」です。
* data_source … 各情報の 出典(文献)のURLです。 | [
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huggingface/documentation-images | huggingface | 2023-12-16T02:11:50Z | 154 | 19 | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:22Z | ---
license: cc-by-nc-sa-4.0
---
### This dataset contains images used in the documentation of HuggingFace's libraries.
HF Team: Please make sure you optimize the assets before uploading them.
My favorite tool for this is https://tinypng.com/.
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xwjiang2010/pile_dedupe_val_tokenized_chunked | xwjiang2010 | 2023-12-12T21:34:33Z | 149 | 0 | [
"region:us"
] | null | 2023-12-11T19:16:42Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 6937220668
num_examples: 423311
download_size: 3163118066
dataset_size: 6937220668
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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chymaks/Igbo_ner | chymaks | 2023-12-12T05:02:33Z | 147 | 0 | [
"license:cc-by-nc-2.0",
"region:us"
] | null | 2023-03-21T14:07:46Z | ---
license: cc-by-nc-2.0
---
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michaelginn/latent-trees-agreement-ID | michaelginn | 2023-12-14T03:34:58Z | 147 | 0 | [
"region:us"
] | null | 2023-11-24T20:32:27Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: depth
dtype: int64
splits:
- name: train
num_bytes: 107176.8
num_examples: 2400
- name: eval
num_bytes: 35725.6
num_examples: 800
- name: test
num_bytes: 35725.6
num_examples: 800
download_size: 56457
dataset_size: 178628.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: test
path: data/test-*
---
# Dataset Card for "latent-trees-agreement-ID"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hoffman-lab/SkyScenes | hoffman-lab | 2023-12-13T10:08:37Z | 147 | 0 | [
"task_categories:object-detection",
"task_categories:depth-estimation",
"task_categories:image-segmentation",
"language:en",
"license:mit",
"dataset",
"aerial",
"synthetic",
"domain adaptation",
"sim2real",
"arxiv:2312.06719",
"region:us"
] | [
"object-detection",
"depth-estimation",
"image-segmentation"
] | 2023-12-01T21:01:18Z | ---
license: mit
language:
- en
task_categories:
- object-detection
- depth-estimation
- image-segmentation
tags:
- dataset
- aerial
- synthetic
- domain adaptation
- sim2real
---
<div align="center">
# SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
[Sahil Khose](https://sahilkhose.github.io/)\*, [Anisha Pal](https://anipal.github.io/)\*, [Aayushi Agarwal](https://www.linkedin.com/in/aayushiag/)\*, [Deepanshi](https://www.linkedin.com/in/deepanshi-d/)\*, [Judy Hoffman](https://faculty.cc.gatech.edu/~judy/), [Prithvijit Chattopadhyay](https://prithv1.xyz/)
</div>
<!-- This repository is the official Pytorch implementation for [SkyScenes](). -->
[](https://huggingface.co/datasets/hoffman-lab/SkyScenes) [](https://github.gatech.edu/pages/hoffman-group/SkyScenes/) [](https://arxiv.org/abs/2312.06719)
<!-- [](./assets/robust_aerial_videos.mp4) -->
<img src="./assets/skyscene_intro_teaser.png" width="100%"/>
## 🚀 Release Updates
Support for downloading the contents of [Images](https://huggingface.co/datasets/hoffman-lab/SkyScenes/tree/main/Images) using [datasets](https://huggingface.co/docs/datasets/index) is under developement and will be made available soon. In the meanwhile it is advised to use wget.
## Dataset Summary
Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions.
Due to inherent challenges in obtaining such images in controlled real-world settings,
we present SkyScenes, a synthetic dataset of densely annotated aerial images captured from Unmanned Aerial Vehicle (UAV) perspectives.
**SkyScenes** images are carefully curated from **CARLA** to comprehensively capture diversity across layout (urban and rural maps), weather conditions, times of day, pitch angles and altitudes with corresponding semantic, instance and depth annotations.
**SkyScenes** features **33,600** images in total, which are spread across 8 towns, 5 weather and daytime conditions and 12 height and pitch variations.
<details>
<summary>Click to view the detailed list of all variations</summary>
- **Layout Variations(Total 8):**:
- Town01
- Town02
- Town03
- Town04
- Town05
- Town06
- Town07
- Town10HD
_Town07 features Rural Scenes, whereas the rest of the towns feature Urban scenes_
- **Weather & Daytime Variations(Total 5):**
- ClearNoon
- ClearSunset
- ClearNight
- CloudyNoon
- MidRainyNoon
- **Height and Pitch Variations of UAV Flight(Total 12):**
- Height = 15m, Pitch = 0°
- Height = 15m, Pitch = 45°
- Height = 15m, Pitch = 60°
- Height = 15m, Pitch = 90°
- Height = 35m, Pitch = 0°
- Height = 35m, Pitch = 45°
- Height = 35m, Pitch = 60°
- Height = 35m, Pitch = 90°
- Height = 60m, Pitch = 0°
- Height = 60m, Pitch = 45°
- Height = 60m, Pitch = 60°
- Height = 60m, Pitch = 90°
</details>
<details>
<summary>Click to view class definitions, color palette and class IDs for Semantic Segmentation</summary>
**SkyScenes** semantic segmentation labels span 28 classes which can be further collapsed to 20 classes.
| Class ID | Class ID (collapsed) | RGB Color Palette | Class Name | Definition |
|----------|--------------------|-------------------|------------------|----------------------------------------------------------------------------------------------------|
| 0 | -1 | <span style="color:rgb(0, 0, 0)"> (0, 0, 0) </span> | unlabeled | Elements/objects in the scene that have not been categorized |
| 1 | 2 | <span style="color:rgb(70, 70, 70)"> (70, 70, 70) </span> | building | Includes houses, skyscrapers, and the elements attached to them |
| 2 | 4 | <span style="color:rgb(190, 153, 153)"> (190, 153, 153) </span> | fence | Wood or wire assemblies that enclose an area of ground |
| 3 | -1 | <span style="color:rgb(55, 90, 80)"> (55, 90, 80) </span> | other | Uncategorized elements |
| 4 | 11 | <span style="color:rgb(220, 20, 60)"> (220, 20, 60) </span> | pedestrian | Humans that walk |
| 5 | 5 | <span style="color:rgb(153, 153, 153)"> (153, 153, 153) </span> | pole | Vertically oriented pole and its horizontal components if any |
| 6 | 16 | <span style="color:rgb(157, 234, 50)"> (157, 234, 50) </span> | roadline | Markings on road |
| 7 | 0 | <span style="color:rgb(128, 64, 128)"> (128, 64, 128) </span> | road | Lanes, streets, paved areas on which cars drive |
| 8 | 1 | <span style="color:rgb(244, 35, 232)"> (244, 35, 232) </span> | sidewalk | Parts of ground designated for pedestrians or cyclists |
| 9 | 8 | <span style="color:rgb(107, 142, 35)"> (107, 142, 35) </span> | vegetation | Trees, hedges, all kinds of vertical vegetation (ground-level vegetation is not included here) |
| 10 | 13 | <span style="color:rgb(0, 0, 142)"> (0, 0, 142) </span> | cars | Cars in scene |
| 11 | 3 | <span style="color:rgb(102, 102, 156)"> (102, 102, 156) </span> | wall | Individual standing walls, not part of buildings |
| 12 | 7 | <span style="color:rgb(220, 220, 0)"> (220, 220, 0) </span> | traffic sign | Signs installed by the state/city authority, usually for traffic regulation |
| 13 | 10 | <span style="color:rgb(70, 130, 180)"> (70, 130, 180) </span> | sky | Open sky, including clouds and sun |
| 14 | -1 | <span style="color:rgb(81, 0, 81)"> (81, 0, 81) </span> | ground | Any horizontal ground-level structures that do not match any other category |
| 15 | -1 | <span style="color:rgb(150, 100, 100)"> (150, 100, 100) </span> | bridge | The structure of the bridge |
| 16 | -1 | <span style="color:rgb(230, 150, 140)"> (230, 150, 140) </span> | railtrack | Rail tracks that are non-drivable by cars |
| 17 | -1 | <span style="color:rgb(180, 165, 180)"> (180, 165, 180) </span> | guardrail | Guard rails / crash barriers |
| 18 | 6 | <span style="color:rgb(250, 170, 30)"> (250, 170, 30) </span> | traffic light | Traffic light boxes without their poles |
| 19 | -1 | <span style="color:rgb(110, 190, 160)"> (110, 190, 160) </span> | static | Elements in the scene and props that are immovable |
| 20 | -1 | <span style="color:rgb(170, 120, 50)"> (170, 120, 50) </span> | dynamic | Elements whose position is susceptible to change over time |
| 21 | 19 | <span style="color:rgb(45, 60, 150)"> (45, 60, 150) </span> | water | Horizontal water surfaces |
| 22 | 9 | <span style="color:rgb(152, 251, 152)"> (152, 251, 152) </span> | terrain | Grass, ground-level vegetation, soil, or sand |
| 23 | 12 | <span style="color:rgb(255, 0, 0)"> (255, 0, 0) </span> | rider | Humans that ride/drive any kind of vehicle or mobility system |
| 24 | 18 | <span style="color:rgb(119, 11, 32)"> (119, 11, 32) </span> | bicycle | Bicycles in scenes |
| 25 | 17 | <span style="color:rgb(0, 0, 230)"> (0, 0, 230) </span> | motorcycle | Motorcycles in scene |
| 26 | 15 | <span style="color:rgb(0, 60, 100)"> (0, 60, 100) </span> | bus | Buses in scenes |
| 27 | 14 | <span style="color:rgb(0, 0, 70)"> (0, 0, 70) </span> | truck | Trucks in scenes |
|
</details>
## Dataset Structure
The dataset is organized in the following structure:
<!--<details>
<summary><strong>Images (RGB Images)</strong></summary>
- ***H_15_P_0***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *ClearSunset*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *ClearNight*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *CloudyNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- *MidRainyNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- ***H_15_P_45***
- ...
- ...
- ***H_60_P_90***
- ...
</details>
<details>
<summary><strong>Instance (Instance Segmentation Annotations)</strong></summary>
- ***H_35_P_45***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
</details>
<details>
<summary><strong>Segment (Semantic Segmentation Annotations)</strong></summary>
- ***H_15_P_0***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
- ***H_15_P_45***
- ...
- ...
- ***H_60_P_90***
</details>
<details>
<summary><strong>Depth (Depth Annotations)</strong></summary>
- ***H_35_P_45***
- *ClearNoon*
- Town01.tar.gz
- Town02.tar.gz
- ...
- Town10HD.tar.gz
</details>
-->
```
├── Images (RGB Images)
│ ├── H_15_P_0
│ │ ├── ClearNoon
│ │ │ ├── Town01
│ │ │ │ └── Town01.tar.gz
│ │ │ ├── Town02
│ │ │ │ └── Town02.tar.gz
│ │ │ ├── ...
│ │ │ └── Town10HD
│ │ │ └── Town10HD.tar.gz
│ │ ├── ClearSunset
│ │ │ ├── Town01
│ │ │ │ └── Town01.tar.gz
│ │ │ ├── Town02
│ │ │ │ └── Town02.tar.gz
│ │ │ ├── ...
│ │ │ └── Town10HD
│ │ │ └── Town10HD.tar.gz
│ │ ├── ClearNight
│ │ │ ├── Town01
│ │ │ │ └── Town01.tar.gz
│ │ │ ├── Town02
│ │ │ │ └── Town02.tar.gz
│ │ │ ├── ...
│ │ │ └── Town10HD
│ │ │ └── Town10HD.tar.gz
│ │ ├── CloudyNoon
│ │ │ ├── Town01
│ │ │ │ └── Town01.tar.gz
│ │ │ ├── Town02
│ │ │ │ └── Town02.tar.gz
│ │ │ ├── ...
│ │ │ └── Town10HD
│ │ │ └── Town10HD.tar.gz
│ │ └── MidRainyNoon
│ │ ├── Town01
│ │ │ └── Town01.tar.gz
│ │ ├── Town02
│ │ │ └── Town02.tar.gz
│ │ ├── ...
│ │ └── Town10HD
│ │ └── Town10HD.tar.gz
│ ├── H_15_P_45
│ │ └── ...
│ ├── ...
│ └── H_60_P_90
│ └── ...
├── Instance (Instance Segmentation Annotations)
│ ├── H_35_P_45
│ │ └── ClearNoon
│ │ ├── Town01
│ │ │ └── Town01.tar.gz
│ │ ├── Town02
│ │ │ └── Town02.tar.gz
│ │ ├── ...
│ │ └── Town10HD
│ │ └── Town10HD.tar.gz
│ └── ...
├── Segment (Semantic Segmentation Annotations)
│ ├── H_15_P_0
│ │ ├── ClearNoon
│ │ │ ├── Town01
│ │ │ │ └── Town01.tar.gz
│ │ │ ├── Town02
│ │ │ │ └── Town02.tar.gz
│ │ │ ├── ...
│ │ │ └── Town10HD
│ │ │ └── Town10HD.tar.gz
│ │ ├── H_15_P_45
│ │ │ └── ...
│ │ ├── ...
│ │ └── H_60_P_90
│ │ └── ...
│ └── ...
└── Depth (Depth Annotations)
├── H_35_P_45
│ └── ClearNoon
│ ├── Town01
│ │ └── Town01.tar.gz
│ ├── Town02
│ │ └── Town02.tar.gz
│ ├── ...
│ └── Town10HD
│ └── Town10HD.tar.gz
└── ...
```
**Note**: Since the same viewpoint is reproduced across each weather variation, hence ClearNoon annotations can be used for all images pertaining to the different weather variations.
## Dataset Download
The dataset can be downloaded using both [datasets](https://huggingface.co/docs/datasets/index) library by Hugging Face and wget.
Since SkyScenes offers variations across different axes we enable different subsets for download that can aid in model sensitivity analysis across these axes.
### Download instructions: wget
**Example script for downloading different subsets of data using wget**
```bash
#!/bin/bash
#Change here to download a specific Height and Pitch Variation, for example - H_15_P_0
#Note- Currently supporting only H_35_P_45 for Instance and Depth Maps
# HP=('H_15_P_45' 'H_15_P_60' 'H_15_P_90')
HP=('H_15_P_0' 'H_15_P_45' 'H_15_P_60' 'H_15_P_90' 'H_35_P_0' 'H_35_P_45' 'H_35_P_60' 'H_35_P_90' 'H_60_P_0' 'H_60_P_45' 'H_60_P_60' 'H_60_P_90')
#Change here to download a specific weather subset, for example - ClearNoon
#Note - For Segment, Instance and Depth annotations this field should only have ClearNoon variation
# weather=('ClearNoon' 'ClearNight')
weather=('ClearNoon' 'ClearNight' 'ClearSunset' 'CloudyNoon' 'MidRainyNoon')
#Change here to download a specific Town subset, for example - Town07
layout=('Town01' 'Town02' 'Town03' 'Town04' 'Town05' 'Town06' 'Town07' 'Town10HD')
#Change here for any specific annotation, for example - https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Segment
base_url=('https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images')
#Change here for base download folder
base_download_folder='SkyScenes'
for hp in "${HP[@]}"; do
for w in "${weather[@]}"; do
for t in "${layout[@]}"; do
folder=$(echo "$base_url" | awk -F '/' '{print $(NF)}')
download_url="${base_url}/${hp}/${w}/${t}/${t}.tar.gz"
download_folder="${base_download_folder}/${folder}/${hp}/${w}/${t}"
mkdir -p "$download_folder"
echo "Downloading: $download_url"
wget -P "$download_folder" "$download_url"
done
done
done
```
### Download instructions: [datasets](https://huggingface.co/docs/datasets/index)
<details>
<summary>Click to view all the available keys for downloading subsets of the data</summary>
* **Layout Variations**
- Rural
- Urban
* **Weather Variations**
- ClearNoon
- ClearNight (only images)
- ClearSunset (only images)
- CloudyNoon (only images)
- MidRainyNoon (only images)
* **Height Variations**
- H_15
- H_35
- H_60
* **Pitch Variations**
- P_0
- P_45
- P_60
- P_90
* **Height and Pitch Variations**
- H_15_P_0
- H_15_P_45
- H_15_P_60
- H_15_P_90
- H_35_P_0
- H_35_P_45
- H_35_P_60
- H_35_P_90
- H_60_P_0
- H_60_P_45
- H_60_P_60
- H_60_P_90
Full dataset key: full
**💡Notes**:
- To download **images** append subset key with **images**, example - ```H_35_P_45 images```
- To download **semantic segmentation** maps append subset key with **semseg**, example - ```H_35_P_45 semseg```
- To download **instance segmentation** maps append subset key with **instance**, example - ```H_35_P_45 instance```
- To download **depth** maps append subset key with **depth**, example - ```H_35_P_45 depth```
</details>
**Example script for loading H_35_P_45 images**
```python
from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 images")
```
**Example script for loading H_35_P_45 semantic segmentation maps**
```python
from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 semseg")
```
**Example script for loading H_35_P_45 instance segmentation maps**
```python
from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 instance")
```
**Example script for loading H_35_P_45 depth maps**
```python
from datasets import load_dataset
dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 depth")
```
### 💡 Notes
- Depth and Instance segmentation maps are available for only H_35_P_45, other variations will be made available soon.
- To prevent issues when loading datasets using [datasets](https://huggingface.co/docs/datasets/index) library, it is recommended to avoid downloading subsets that contain overlapping directories. If there are any overlapping directories between the existing downloads and new ones, it's essential to clear the .cache directory of any such overlaps before proceeding with the new downloads. This step will ensure a clean and conflict-free environment for handling datasets.
## BibTex
If you find this work useful please like ❤️ our dataset repo and cite 📄 our paper. Thanks for your support!
```
@misc{khose2023skyscenes,
title={SkyScenes: A Synthetic Dataset for Aerial Scene Understanding},
author={Sahil Khose and Anisha Pal and Aayushi Agarwal and Deepanshi and Judy Hoffman and Prithvijit Chattopadhyay},
year={2023},
eprint={2312.06719},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` | [
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tyouisen/aclue | tyouisen | 2023-12-12T06:07:10Z | 146 | 2 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:1M<n<10M",
"language:zh",
"license:cc-by-nc-4.0",
"llm",
"Ancient Chinese",
"Evaluation",
"chinese",
"arxiv:2310.0955",
"region:us"
] | [
"multiple-choice",
"question-answering"
] | 2023-08-16T14:14:21Z | ---
license: cc-by-nc-4.0
task_categories:
- multiple-choice
- question-answering
language:
- zh
tags:
- llm
- Ancient Chinese
- Evaluation
- chinese
pretty_name: ACLUE
size_categories:
- 1M<n<10M
---
# Dataset Card for ACLUE
- **Homepage:** [https://github.com/isen-zhang/ACLUE](https://github.com/isen-zhang/ACLUE)
- **Repository:** [https://huggingface.co/datasets/tyouisen/aclue](https://huggingface.co/datasets/tyouisen/aclue)
- **Paper:** [https://arxiv.org/abs/2310.0955](https://arxiv.org/abs/2310.0955)
- **Leaderboard:** [https://github.com/isen-zhang/ACLUE](https://github.com/isen-zhang/ACLUE)
### 简介 (Introduction)
Ancient Chinese Language Understanding Evaluation (ACLUE) 是一个面向古代汉语的评估基准,旨在帮助评估大型语言模型在古代汉语上的表现。
The Ancient Chinese Language Understanding Evaluation (ACLUE) is an evaluation benchmark focused on ancient Chinese language comprehension. It aims to assess the performance of large-scale language models (LLMs) on understanding ancient Chinese.
### 数据 (Data)
该基准测试包含15个任务,涵盖了各个领域,包括词汇、句法、语义、推理和知识。我们为这15个任务提供了开发集和测试集数据,开发集中有5个问题,而测试集中则有100多个问题。我们鼓励研究人员使用ACLUE来测试和提升其模型在古代汉语语言理解方面的能力。ACLUE的任务取自人工挑选的公开资源和自动生成的古代汉语语料库。这些问题涵盖了从夏朝(公元前2070年)到明朝(公元1368年)的广泛时间范围。ACLUE对所有任务都采用了多项选择题的形式。
The benchmark comprises 15 tasks spanning various domains, including lexical, syntactic, semantic, inference, and knowledge. We provide development and test dataset for each of 15 tasks, with 5 questions in development set and 100+ quesitons in test set. We encourage researchers to use ACLUE to test and enhance their models' abilities in ancient Chinese language understanding. ACLUE's tasks are derived from a combination of manually curated questions from publicly available resources, and automatic generated questions from classical Chinese language corpora. The range of questions span from the Xia dynasty (2070 BCE) to the Ming dynasty (1368 CE). ACLUE employs a multiple-choice question format for all tasks.
### 数据实例( Data Instances)
数据集中的每个问题都是一个包含4个选项的多项选择题,其中只有一个选项是正确答案。以下是两个示例:
Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer. Here are two examples:
```
以下是关于{古诗词曲鉴赏}的单项选择题,请直接给出正确答案的选项。
题目:《木兰诗--北朝民歌》唧唧复唧唧,木兰当户织。不闻机杼声,唯闻女叹息。问女何所思,问女何所忆。女亦无所思,女亦无所忆。昨夜见军帖,可汗大点兵,军书十二卷,卷卷有爷名。阿爷无大儿,木兰无长兄,愿为市鞍马,从此替爷征。东市买骏马,西市买鞍鞯,南市买辔头,北市买长鞭。旦辞爷娘去,暮宿黄河边,不闻爷娘唤女声,但闻黄河流水鸣溅溅。旦辞黄河去,暮至黑山头,不闻爷娘唤女声,但闻燕山胡骑鸣啾啾。万里赴戎机,关山度若飞。朔气传金柝,寒光照铁衣。将军百战死,壮士十年归。归来见天子,天子坐明堂。策勋十二转,赏赐百千强。可汗问所欲,木兰不用尚书郎,愿驰千里足,送儿还故乡。爷娘闻女来,出郭相扶将;阿姊闻妹来,当户理红妆;小弟闻姊来,磨刀霍霍向猪羊。开我东阁门,坐我西阁床。脱我战时袍,著我旧时裳。当窗理云鬓,对镜帖花黄。出门看火伴,火伴皆惊忙:同行十二年,不知木兰是女郎。雄兔脚扑朔,雌兔眼迷离;双兔傍地走,安能辨我是雄雌?下列对这首诗的理解和分析,不正确的一项是 ()
A. 《木兰诗》是南北朝时期的一首长篇叙事民歌,风格刚健质朴。全诗以“木兰是女郎”来构思木兰的传奇故事,富有浪漫色彩。
B. “愿为市鞍马”的“市”是“市场”的意思,“万里赴戎机”的“戎机”是“战事”的意思。
C. 木兰“不用尚书郎”而愿“还故乡”固然有对家乡的眷恋,但也有自己女儿身秘密的因素。
D. “朔气传金柝,寒光照铁衣”运用对偶手法,描写了木兰在边塞艰苦的军旅生活。
答案是:B
```
```
题目:《虞美人》李煜。春花秋月何时了?往事知多少。小楼昨夜又东风,故国不堪回首月明中。雕栏玉砌应犹在,只是朱颜改。问君能有几多愁?恰似一江春水向东流。对《虞美人》的赏析,不恰当的一项是()
A. 词作从眼前景物入手,生发联想和想像,追怀昔日帝王生活,描摹了一幅幅鲜活的画面,隐晦地表达出叛逆之情,惹恼了宋太宗,铸成了词人悲惨结局。
B. 词作以实虚相间的手法来绘景、抒情、达意,忽而写眼前,忽而写想像。
C. 《虞美人》乃李煜绝笔词
D. 《虞美人》以其形式别致给人美感愉悦。
答案是:
```
以下列出了任务的类别、实例数量、问题平均长度以及任务的来源:
The category, number of instances, average length of the question, and origin of the tasks are provided below:
| Task | Total Q. | Avg. len |Task (zh) | Category | Origin |
|-------------------------------|------|------|-----------------------------------|----------|-----------|
| Named entity recognition | 500 | 138 | 古汉语命名体识别 | lexical | generated |
| Polysemy resolution | 500 | 116 | 古文单字多义 | lexical | generated |
| Homographic character resolution | 500 | 137 | 通假字 | lexical | generated |
| Sentence segmentation | 500 | 210 | 古文断句 | syntactic| generated |
| Couplet prediction | 500 | 62 | 对联预测 | semantic | generated |
| Poetry context prediction | 500 | 77 | 古诗词上下句预测 | semantic | generated |
| Poetry sentiment analysis | 500 | 60 | 诗词情感分类 | inference| generated |
| Poem quality estimation | 406 | 118 | 古诗词质量评估 | inference| generated |
| Ancient Chinese medical | 211 | 38 | 医古文 | knowledge| collected |
| Ancient Chinese literature | 160 | 44 | 古代文学知识 | knowledge| collected |
| Traditional Chinese culture | 136 | 59 | 国学常识 | knowledge| collected |
| Poetry appreciation | 103 | 258 | 古诗词曲鉴赏 | inference| collected |
| Basic ancient Chinese | 249 | 52 | 基础古汉语知识 | knowledge| collected |
| Reading comprehension | 101 | 982 | 古文阅读理解 | inference| collected |
| Ancient Chinese phonetics | 101 | 50 | 古音学 | knowledge| collected |
#### 加载数据 (Load data)
```python
task_list = ['polysemy_resolution',
'poetry_sentiment_analysis',
'named_entity_recognition',
'basic_ancient_chinese',
'poetry_context_prediction',
'sentence_segmentation',
'couplet_prediction',
'poetry_appreciate',
'ancient_chinese_culture',
'ancient_phonetics',
'homographic_character_resolution',
'ancient_literature',
'ancient_medical',
'poetry_quality_assessment',
'reading_comprehension']
from datasets import load_dataset
dataset = {k: load_dataset(r"tyouisen/aclue", k) for k in task_list}
# Print an example:
print(dataset['polysemy_resolution']['test'][0])
# Or download specific dataset:
dataset = load_dataset("tyouisen/aclue", "couplet_prediction", split="test") # or split = "dev"
```
### 引用 (Citation)
```
Zhang, Yixuan, and Haonan Li. "Can Large Langauge Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE." Proceedings of the Ancient Language Processing Workshop. 2023.
```
### 许可证 (License)
ACLUE数据集采用:(The ACLUE dataset is licensed under a:)
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
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seonglae/nq_open-validation | seonglae | 2023-12-14T10:23:53Z | 140 | 0 | [
"region:us"
] | null | 2023-11-15T18:04:11Z | ---
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path: psgs_w100.dpr_nq.4_resrer-bart.1_dpr-reader-single-nq-base/train-*
---
# Dataset Card for "nq_open-validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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svenschultze/artificial-vs-natural | svenschultze | 2023-12-12T11:13:22Z | 137 | 0 | [
"region:us"
] | null | 2023-12-07T15:03:10Z | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: margin
dtype: int64
splits:
- name: train
num_bytes: 26691782.61397505
num_examples: 8153
- name: test
num_bytes: 2966117.3860249477
num_examples: 906
download_size: 15568743
dataset_size: 29657900.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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SonishMaharjan/asr | SonishMaharjan | 2023-12-14T03:21:11Z | 136 | 0 | [
"license:unknown",
"region:us"
] | null | 2023-12-13T09:08:29Z | ---
license: unknown
---
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Antreas/TALI | Antreas | 2023-12-13T09:02:28Z | 130 | 1 | [
"task_categories:zero-shot-classification",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"video",
"audio",
"text",
"image",
"tetramodal",
"multimodal",
"youtube",
"wikipedia",
"region:us"
] | [
"zero-shot-classification"
] | 2023-08-16T22:59:13Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: image_url
dtype: string
- name: item_idx
dtype: int64
- name: wit_features
struct:
- name: attribution_passes_lang_id
sequence: bool
- name: caption_alt_text_description
sequence: string
- name: caption_reference_description
sequence: string
- name: caption_title_and_reference_description
sequence: string
- name: context_page_description
sequence: string
- name: context_section_description
sequence: string
- name: hierarchical_section_title
sequence: string
- name: is_main_image
sequence: bool
- name: language
sequence: string
- name: page_changed_recently
sequence: bool
- name: page_title
sequence: string
- name: page_url
sequence: string
- name: section_title
sequence: string
- name: wit_idx
dtype: int64
- name: youtube_title_text
dtype: string
- name: youtube_description_text
dtype: string
- name: youtube_video_content
dtype: binary
- name: youtube_video_starting_time
dtype: string
- name: youtube_subtitle_text
dtype: string
- name: youtube_video_size
dtype: int64
- name: youtube_video_file_path
dtype: string
splits:
- name: train
num_bytes: 1902638101655.625
num_examples: 1052915
- name: val
num_bytes: 104485442867.25
num_examples: 57958
- name: test
num_bytes: 111107332347.375
num_examples: 61389
download_size: 2058391040534
dataset_size: 2118230876870.25
license: cc-by-4.0
task_categories:
- zero-shot-classification
tags:
- video
- audio
- text
- image
- tetramodal
- multimodal
- youtube
- wikipedia
pretty_name: TALI
size_categories:
- 1M<n<10M
---
# Dataset Card for "TALI"
## Table of Contents
1. Dataset Description
1. Abstract
2. Brief Description
2. Dataset Information
1. Modalities
2. Dataset Variants
3. Dataset Statistics
4. Data Fields
5. Data Splits
3. Dataset Creation
4. Dataset Use
5. Additional Information
## Dataset Description
### Abstract
TALI is a large-scale, tetramodal dataset designed to facilitate a shift from unimodal and duomodal to tetramodal research in deep learning. It aligns text, video, images, and audio, providing a rich resource for innovative self-supervised learning tasks and multimodal research. TALI enables exploration of how different modalities and data/model scaling affect downstream performance, with the aim of inspiring diverse research ideas and enhancing understanding of model capabilities and robustness in deep learning.
### Brief Description
TALI (Temporally and semantically Aligned Audio, Language and Images) is a dataset that uses the Wikipedia Image Text (WIT) captions and article titles to search Youtube for videos that match the captions. It then downloads the video, audio, and subtitles from these videos. The result is a rich multimodal dataset that has multiple caption types related to both the WiT Images, and the Youtube videos. This enables learning to take place between either temporally or semantically aligned text, images, audio and video.
## Dataset Information
### Modalities
The TALI dataset consists of the following modalities:
1. Image:
1. Wikipedia caption image
2. Randomly sampled image from youtube video
2. Text
1. Wikipedia Caption Text
2. Wikipedia Title Text
3. Wikipedia Main Body Text
4. YouTube Subtitle Text
5. YouTube Description Text
6. YouTube Title Text
3. Audio
1. YouTube Content Audio
4. Video
1. YouTube Content Video
## Usage:
To get started with TALI, you can load the dataset via Hugging Face's `datasets` library through our helper functions. The reason we don't use `datasets` directly is because we found huggingface_hub downloads much faster and reliable. For a full set of possible configurations look at [examples.py](examples.py). Here's a basic usage example:
First install the tali package:
### Installation
For the default install use:
```bash
pip install git+https://github.com/AntreasAntoniou/TALI
```
For the dev install use:
```bash
pip install git+https://github.com/AntreasAntoniou/TALI[dev]
```
Then use the dataset using:
### Examples
Import relevant helper functions
```python
import pathlib
from enum import Enum
import torch
from tqdm.auto import tqdm
from tali.data import (
SubModalityTypes,
TALIBaseTransform,
TALIBaseTransformConfig,
VideoFramesFormat,
default_transforms,
load_dataset_via_hub,
)
```
#### TALI with default transforms (CLIP and Whisper) and no streaming
```python
def tali_with_transforms_no_streaming(
dataset_storage_path: pathlib.Path | str,
):
if isinstance(dataset_storage_path, str):
dataset_storage_path = pathlib.Path(dataset_storage_path)
dataset = load_dataset_via_hub(
dataset_storage_path, dataset_name="Antreas/TALI"
)["train"]
(
image_transforms,
text_transforms,
audio_transforms,
video_transforms,
) = default_transforms()
preprocessing_transform = TALIBaseTransform(
cache_dir=dataset_storage_path / "cache",
text_tokenizer=text_transforms,
image_tokenizer=image_transforms,
audio_tokenizer=audio_transforms,
video_tokenizer=video_transforms,
config=TALIBaseTransformConfig(
root_filepath=dataset_storage_path,
modality_list=[
SubModalityTypes.youtube_content_video,
SubModalityTypes.youtube_content_audio,
SubModalityTypes.youtube_random_video_frame,
SubModalityTypes.youtube_subtitle_text,
SubModalityTypes.youtube_description_text,
SubModalityTypes.youtube_title_text,
SubModalityTypes.wikipedia_caption_image,
SubModalityTypes.wikipedia_caption_text,
SubModalityTypes.wikipedia_main_body_text,
SubModalityTypes.wikipedia_title_text,
],
video_frames_format=VideoFramesFormat.PIL,
),
)
for sample in tqdm(dataset):
sample = preprocessing_transform(sample)
print(list(sample.keys()))
for key, value in sample.items():
if hasattr(value, "shape"):
print(key, value.shape)
elif isinstance(value, torch.Tensor):
print(key, value.shape)
elif hasattr(value, "__len__"):
print(key, len(value))
print(key, type(value))
break
```
#### TALI with no transforms and no streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats
```python
def tali_without_transforms_no_streaming(
dataset_storage_path: pathlib.Path | str,
):
if isinstance(dataset_storage_path, str):
dataset_storage_path = pathlib.Path(dataset_storage_path)
dataset = load_dataset_via_hub(
dataset_storage_path, dataset_name="Antreas/TALI"
)["train"]
preprocessing_transform = TALIBaseTransform(
cache_dir=dataset_storage_path / "cache",
text_tokenizer=None,
image_tokenizer=None,
audio_tokenizer=None,
video_tokenizer=None,
config=TALIBaseTransformConfig(
root_filepath=dataset_storage_path,
modality_list=[
SubModalityTypes.youtube_content_video,
SubModalityTypes.youtube_content_audio,
SubModalityTypes.youtube_random_video_frame,
SubModalityTypes.youtube_subtitle_text,
SubModalityTypes.youtube_description_text,
SubModalityTypes.youtube_title_text,
SubModalityTypes.wikipedia_caption_image,
SubModalityTypes.wikipedia_caption_text,
SubModalityTypes.wikipedia_main_body_text,
SubModalityTypes.wikipedia_title_text,
],
video_frames_format=VideoFramesFormat.PIL,
),
)
for sample in tqdm(dataset):
sample = preprocessing_transform(sample)
print(list(sample.keys()))
for key, value in sample.items():
if hasattr(value, "shape"):
print(key, value.shape)
elif isinstance(value, torch.Tensor):
print(key, value.shape)
elif hasattr(value, "__len__"):
print(key, len(value))
print(key, type(value))
break
```
#### TALI with default transforms and streaming
```python
def tali_with_transforms_streaming(
dataset_storage_path: pathlib.Path | str,
):
if isinstance(dataset_storage_path, str):
dataset_storage_path = pathlib.Path(dataset_storage_path)
dataset = load_dataset_via_hub(
dataset_storage_path, dataset_name="Antreas/TALI", streaming=True
)["train"]
(
image_transforms,
text_transforms,
audio_transforms,
video_transforms,
) = default_transforms()
preprocessing_transform = TALIBaseTransform(
cache_dir=dataset_storage_path / "cache",
text_tokenizer=text_transforms,
image_tokenizer=image_transforms,
audio_tokenizer=audio_transforms,
video_tokenizer=video_transforms,
config=TALIBaseTransformConfig(
root_filepath=dataset_storage_path,
modality_list=[
SubModalityTypes.youtube_content_video,
SubModalityTypes.youtube_content_audio,
SubModalityTypes.youtube_random_video_frame,
SubModalityTypes.youtube_subtitle_text,
SubModalityTypes.youtube_description_text,
SubModalityTypes.youtube_title_text,
SubModalityTypes.wikipedia_caption_image,
SubModalityTypes.wikipedia_caption_text,
SubModalityTypes.wikipedia_main_body_text,
SubModalityTypes.wikipedia_title_text,
],
video_frames_format=VideoFramesFormat.PIL,
),
)
for sample in tqdm(dataset):
sample = preprocessing_transform(sample)
print(list(sample.keys()))
for key, value in sample.items():
if hasattr(value, "shape"):
print(key, value.shape)
elif isinstance(value, torch.Tensor):
print(key, value.shape)
elif hasattr(value, "__len__"):
print(key, len(value))
print(key, type(value))
break
```
#### TALI with no transforms and streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats
```python
def tali_without_transforms_streaming(
dataset_storage_path: pathlib.Path | str,
):
if isinstance(dataset_storage_path, str):
dataset_storage_path = pathlib.Path(dataset_storage_path)
dataset = load_dataset_via_hub(
dataset_storage_path, dataset_name="Antreas/TALI", streaming=True
)["train"]
preprocessing_transform = TALIBaseTransform(
cache_dir=dataset_storage_path / "cache",
text_tokenizer=None,
image_tokenizer=None,
audio_tokenizer=None,
video_tokenizer=None,
config=TALIBaseTransformConfig(
root_filepath=dataset_storage_path,
modality_list=[
SubModalityTypes.youtube_content_video,
SubModalityTypes.youtube_content_audio,
SubModalityTypes.youtube_random_video_frame,
SubModalityTypes.youtube_subtitle_text,
SubModalityTypes.youtube_description_text,
SubModalityTypes.youtube_title_text,
SubModalityTypes.wikipedia_caption_image,
SubModalityTypes.wikipedia_caption_text,
SubModalityTypes.wikipedia_main_body_text,
SubModalityTypes.wikipedia_title_text,
],
video_frames_format=VideoFramesFormat.PIL,
),
)
for sample in tqdm(dataset):
sample = preprocessing_transform(sample)
print(list(sample.keys()))
for key, value in sample.items():
if hasattr(value, "shape"):
print(key, value.shape)
elif isinstance(value, torch.Tensor):
print(key, value.shape)
elif hasattr(value, "__len__"):
print(key, len(value))
print(key, type(value))
break
```
### Dataset Statistics
TBA
## Dataset Creation
The TALI dataset was created by starting from the WiT dataset and using either the context_page_description or page_title as a source-query to search YouTube for video that were creative commons opted-in, and, not age restricted. The top 100 result titles were returned and compared with the source-query using the CLIP text embeddings of the largest CLIP model available. The top-1 title’s video based on the CLIP ranking was chosen and downloaded. The video was broken into 30-second segments and the top-10 segments for eachvideo were chosen based on the distance between the CLIP image embedding of the first image of each segment and the video’s title text. The image, audio, and subtitle frames were extracted from these segments. At sampling time, one of these 10 segments is randomly selected, and a 10-second segment is chosen out of the 30-second clip. The result is 200 video frames (spread throughout the 10-second segment), and 160000 audio frames (10 seconds).
## Dataset Use
TALI is designed for use in a wide range of multimodal research tasks, including but not limited to:
- Multimodal understanding and reasoning
- Self-supervised learning
- Multimodal alignment and translation
- Multimodal summarization
- Multimodal question answering
## Dataset Curators: Antreas Antoniou
Citation Information: TBA
Contributions: Thanks to all contributors including data curators, annotators, and software developers.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nesuri/sorsolingo-tts-bsl | nesuri | 2023-12-17T13:57:19Z | 129 | 0 | [
"region:us"
] | null | 2023-12-07T14:37:55Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 82617979.0
num_examples: 153
download_size: 79264687
dataset_size: 82617979.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ddellapietra/test1 | ddellapietra | 2023-12-17T00:06:28Z | 129 | 0 | [
"region:us"
] | null | 2023-12-16T21:49:17Z | ---
dataset_info:
features:
- name: formatted_prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 20786720
num_examples: 1000
download_size: 8512059
dataset_size: 20786720
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Pipper/sol_processed_s2s | Pipper | 2023-12-12T09:57:39Z | 127 | 0 | [
"region:us"
] | null | 2023-11-16T09:41:34Z | ---
dataset_info:
features:
- name: file_name
dtype: string
- name: comments
dtype: string
- name: code_string
dtype: string
- name: __index_level_0__
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 48324923815
num_examples: 1223464
- name: test
num_bytes: 2669251521
num_examples: 67971
- name: valid
num_bytes: 2660994665
num_examples: 67970
download_size: 11579574597
dataset_size: 53655170001
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sol_processed_s2s"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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librarian-bots/model_cards_with_metadata_with_embeddings | librarian-bots | 2023-12-11T10:06:20Z | 127 | 5 | [
"task_categories:text-retrieval",
"size_categories:100K<n<1M",
"region:us"
] | [
"text-retrieval"
] | 2023-11-30T18:37:44Z | ---
size_categories:
- 100K<n<1M
task_categories:
- text-retrieval
pretty_name: Model Card
dataset_info:
features:
- name: modelId
dtype: string
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: library_name
dtype: string
- name: tags
sequence: string
- name: pipeline_tag
dtype: string
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: card
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 2043696915.96232
num_examples: 432043
download_size: 1200724193
dataset_size: 2043696915.96232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Hugging Face Hub Model Cards with Embeddings
This dataset consists of [model cards](https://huggingface.co/docs/hub/model-cards) for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more.
This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new [discussion](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/discussions/new).
This dataset is the same as the [Hugging Face Hub Model Cards](https://huggingface.co/datasets/librarian-bots/model_cards) dataset but with the addition of embeddings for each model card. The embeddings are generated using the [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) model.
## Dataset Details
### Dataset Description
- **Curated by:** Daniel van Strien
- **Language(s) (NLP):** Model cards on the Hugging Face Hub are predominantly in English but may include other languages.
## Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in model cards
- analysis of the model card format/content
- topic modelling of model cards
- analysis of the model card metadata
- training language models on model cards
- build a recommender system for model cards
- build a search engine for model cards
### Out-of-Scope Use
[More Information Needed]
## Dataset Structure
This dataset has a single split.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The dataset was created to assist people in working with model cards. In particular. it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.
### Source Data
The source data is `README.md` files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
The data is downloaded using a CRON job on a daily basis.
#### Who are the source data producers?
The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.
### Annotations [optional]
There are no additional annotations in this dataset beyond the model card content.
#### Annotation process
N/A
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
N/A
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards.
Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
## Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
## Dataset Card Authors
[@davanstrien](https://huggingface.co/davanstrien)
## Dataset Card Contact
[@davanstrien](https://huggingface.co/davanstrien) | [
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librarian-bots/dataset_cards_with_metadata | librarian-bots | 2023-12-17T01:45:28Z | 123 | 6 | [
"task_categories:text-retrieval",
"size_categories:10K<n<100K",
"ethics",
"documentation",
"region:us"
] | [
"text-retrieval"
] | 2023-10-11T09:15:10Z | ---
size_categories:
- 10K<n<100K
task_categories:
- text-retrieval
dataset_info:
features:
- name: datasetId
dtype: string
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: tags
sequence: string
- name: task_categories
sequence: string
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: card
dtype: string
splits:
- name: train
num_bytes: 214327992
num_examples: 57583
download_size: 53399171
dataset_size: 214327992
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- ethics
- documentation
---
# Dataset Card for Hugging Face Hub Dataset Cards
This datasets consists of [dataset cards](https://huggingface.co/docs/hub/datasets-cards) for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub.
This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new [discussion](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/discussions/new).
## Dataset Details
### Dataset Description
- **Curated by:** Daniel van Strien
- **Language(s) (NLP):** Dataset cards on the Hugging Face Hub are predominantly in English but may include other languages.
## Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in dataset cards
- analysis of the dataset card format/content
- topic modelling of dataset cards
- training language models on the dataset cards
### Out-of-Scope Use
[More Information Needed]
## Dataset Structure
This dataset has a single split.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.
### Source Data
The source data is `README.md` files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
The data is downloaded using a CRON job on a daily basis.
#### Who are the source data producers?
The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.
### Annotations [optional]
There are no additional annotations in this dataset beyond the dataset card content.
#### Annotation process
N/A
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
N/A
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards.
Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
## Dataset Card Authors
[@davanstrien](https://huggingface.co/davanstrien)
## Dataset Card Contact
[@davanstrien](https://huggingface.co/davanstrien) | [
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binwang/InstructDS_datasets | binwang | 2023-12-16T03:29:54Z | 119 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-16T03:13:27Z | ---
license: cc-by-nc-4.0
configs:
- config_name: SAMSum
data_files:
- split: train
path: "SAMSum/train.json"
- split: validation
path: "SAMSum/validation.json"
- split: test
path: "SAMSum/test.json"
- config_name: SAMSum_QDS
data_files:
- split: train
path: "SAMSum_QDS/train.json"
- split: validation
path: "SAMSum_QDS/validation.json"
- split: test
path: "SAMSum_QDS/test.json"
- config_name: DialogSum
data_files:
- split: train
path: "DialogSum/train.json"
- split: validation
path: "DialogSum/validation.json"
- split: test
path: "DialogSum/test.json"
- config_name: DialogSum_QDS
data_files:
- split: train
path: "DialogSum_QDS/train.json"
- split: validation
path: "DialogSum_QDS/validation.json"
- split: test
path: "DialogSum_QDS/test.json"
- config_name: TODSum
data_files:
- split: train
path: "TODSum/train.json"
- split: validation
path: "TODSum/validation.json"
- split: test
path: "TODSum/test.json"
- config_name: TODSum_QDS
data_files:
- split: train
path: "TODSum_QDS/train.json"
- split: validation
path: "TODSum_QDS/validation.json"
- split: test
path: "TODSum_QDS/test.json"
- config_name: DREAM
data_files:
- split: train
path: "DREAM/train.json"
- split: validation
path: "DREAM/validation.json"
- split: test
path: "DREAM/test.json"
---
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TimKoornstra/financial-tweets-sentiment | TimKoornstra | 2023-12-15T18:20:16Z | 117 | 2 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"sentiment",
"twitter",
"finance",
"crypto",
"stocks",
"tweet",
"collection",
"region:us"
] | [
"text-classification"
] | 2023-12-09T17:03:27Z | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-classification
pretty_name: Financial Tweets with Sentiment class
dataset_info:
features:
- name: tweet
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': neutral
'1': bullish
'2': bearish
- name: url
dtype: string
splits:
- name: train
num_bytes: 6848991
num_examples: 38091
download_size: 2648055
dataset_size: 6848991
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- sentiment
- twitter
- finance
- crypto
- stocks
- tweet
- collection
---
# Financial Sentiment Analysis Dataset
## Overview
This dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector.
## Data Description
The dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency.
### Dataset Structure
- **Tweet**: The text of the tweet, providing insights into financial discussions.
- **Sentiment**: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral).
## Dataset Size
- **Bullish Sentiments**: 17,368
- **Bearish Sentiments**: 14,383
- **Neutral Sentiments**: 6,340
## Sources
This dataset is an amalgamation of data from various reputable sources, each contributing a unique perspective on financial sentiment:
- [FIQA Sentiment Classification](https://huggingface.co/datasets/ChanceFocus/fiqa-sentiment-classification): A sentiment analysis dataset with 721 positive, 379 negative, and 11 neutral sentiments.
- [Stock Market Tweets Data](https://ieee-dataport.org/open-access/stock-market-tweets-data): A collection of tweets with 523 positive, 420 neutral, and 341 negative sentiments.
- [Stock Related Tweet Sentiment](https://www.kaggle.com/datasets/mattgilgo/stock-related-tweet-sentiment): A dataset featuring 5005 positive, 741 neutral, and 736 negative sentiments.
- [Master Thesis Data](https://github.com/moritzwilksch/MasterThesis/tree/main): Includes 3711 positive, 2784 neutral, and 2167 negative sentiments.
- [Twitter Stock Sentiment](https://github.com/poojathakoor/twitter-stock-sentiment): Comprises 702 positive, 595 negative, and 481 neutral sentiments.
- [Crypto Sentiment](https://github.com/surge-ai/crypto-sentiment/tree/main): Sentiment data for cryptocurrency-related tweets with 296 positive and 256 negative sentiments.
- [Stock Sentiment](https://github.com/surge-ai/stock-sentiment/tree/main): Sentiment analysis on stock-related tweets, including 327 positive and 173 negative sentiments.
- [Stockmarket Sentiment Dataset](https://www.kaggle.com/datasets/yash612/stockmarket-sentiment-dataset): Features 3685 positive and 2106 negative sentiments.
- [Twitter Financial News Sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment): Contains 2398 positive, 7744 negative, and 1789 neutral sentiments.
## Usage
This dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions.
## Acknowledgments
We extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource.
## License
This dataset is made available under the MIT license, adhering to the licensing terms of the original datasets. | [
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bzantium/LongPerplexity | bzantium | 2023-12-11T07:46:20Z | 117 | 0 | [
"language:en",
"license:apache-2.0",
"ppl",
"region:us"
] | null | 2023-12-11T07:34:25Z | ---
license: apache-2.0
configs:
- config_name: c4
data_files:
- split: test
path: c4.jsonl
- config_name: arxiv
data_files:
- split: test
path: arxiv.jsonl
- config_name: github
data_files:
- split: test
path: github.jsonl
language:
- en
tags:
- ppl
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Jiahuan/teach_edh | Jiahuan | 2023-12-13T15:12:08Z | 114 | 0 | [
"region:us"
] | null | 2023-11-28T19:57:13Z | ---
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 3300795
num_examples: 9742
- name: validation
num_bytes: 357450
num_examples: 1137
- name: test
num_bytes: 1104161
num_examples: 3855
download_size: 1752273
dataset_size: 4762406
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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agil/EUIPO_questions_enhanced | agil | 2023-12-17T19:02:17Z | 114 | 0 | [
"region:us"
] | null | 2023-12-12T12:40:33Z | ---
dataset_info:
features:
- name: ID_oq
dtype: int64
- name: text_oq
dtype: string
- name: text_gq
dtype: string
- name: equiv
dtype: int64
splits:
- name: train
num_bytes: 360653.6
num_examples: 2316
- name: test
num_bytes: 90163.4
num_examples: 579
download_size: 157976
dataset_size: 450817.0
---
# Dataset Card for "EUIPO_questions_enhanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jxm/msmarco__gtr_base__dpr | jxm | 2023-12-12T23:13:55Z | 114 | 0 | [
"region:us"
] | null | 2023-12-12T23:13:30Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 647745307
num_examples: 100000
download_size: 757450091
dataset_size: 647745307
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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gordicaleksa/serbian-llm-eval-v1 | gordicaleksa | 2023-12-17T22:28:38Z | 111 | 0 | [
"language:sr",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T20:53:51Z | ---
license: apache-2.0
language:
- sr
pretty_name: s
---
# Serbian LLM eval 🇷🇸
This dataset should be used for Serbian (and potentially also HBS) LLM evaluation.
[GitHub project](https://github.com/gordicaleksa/lm-evaluation-harness-serbian) used to build this dataset.
For technical report of the project see this in-depth [Weights & Biases report](https://wandb.ai/gordicaleksa/serbian_llm_eval/reports/First-Serbian-LLM-eval---Vmlldzo2MjgwMDA5). ❤️
TL;DR:
## What is covered?
Common sense reasoning:
* Hellaswag, Winogrande, PIQA, OpenbookQA, ARC-Easy, ARC-Challenge
World knowledge:
* NaturalQuestions, TriviaQA
Reading comprehension:
* BoolQ
## How was the eval created?
3 steps:
1. Machine Translation from English -> Serbian using Google Translate (currently the best system for this lang direction)
2. Refinement via GPT-4
3. Minor manual work by me (Aleksa Gordić) + we'll likely have a new version of Winogrande that was annotated by a human annotator
Please see [the report](https://wandb.ai/gordicaleksa/serbian_llm_eval/reports/First-Serbian-LLM-eval---Vmlldzo2MjgwMDA5) for more detail.
## Example of how to use
1. Create a python environment and install HuggingFace datasets (`pip install datasets`).
2. Run:
```Python
import datasets
tasks = ["arc_challenge", "arc_easy", "boolq", "hellaswag", "nq_open", "openbookqa", "piqa", "triviaqa", "winogrande"]
for task in tasks:
dataset = datasets.load_dataset("gordicaleksa/serbian-llm-eval-v1", task)
for split in dataset.keys():
dataset = dataset[split]
print(f"Task: {task}, Split: {split}")
for example in dataset:
print(example)
```
# Project Sponsors
## Platinum sponsors 🌟
* <b>Ivan</b> (fizicko lice, anoniman)
## Gold sponsors 🟡
* **qq** (fizicko lice, anoniman)
* [**Mitar Perovic**](https://www.linkedin.com/in/perovicmitar/)
* [**Nikola Ivancevic**](https://www.linkedin.com/in/nivancevic/)
## Silver sponsors ⚪
[**psk.rs**](https://psk.rs/), [**OmniStreak**](https://omnistreak.com/), [**Marko Radojicic**](https://www.linkedin.com/in/marko-radojicic-acmanik-cube/), [**Luka Vazic**](https://www.linkedin.com/in/vazic/), [**Miloš Durković**](https://www.linkedin.com/in/milo%C5%A1-d-684b99188/), [**Marjan Radeski**](https://www.linkedin.com/in/marjanradeski/), **Marjan Stankovic** (fizicko lice), [**Nikola Stojiljkovic**](https://www.linkedin.com/in/nikola-stojiljkovic-10469239/), [**Mihailo Tomic**](https://www.linkedin.com/in/mihailotomic/), [**Bojan Jevtic**](https://www.linkedin.com/in/bojanjevtic/), [**Jelena Jovanović**](https://www.linkedin.com/in/eldumo/), [**Nenad Davidović**](https://www.linkedin.com/in/nenad-davidovic-662ab749/)
## Credits
Thank you to the following individuals from my [Discord server](https://discord.gg/peBrCpheKE
) who helped with donating google translate creditss & running machine translation part of the pipeline:
[Vera Prohaska](https://vtwoptwo.com/), [Chu Kin Chan](www.linkedin.com/in/roy-ck-chan), [Joe Makepeace](https://www.linkedin.com/in/joe-makepeace-a872a1183/), [Toby Farmer](https://www.linkedin.com/in/tobyfarmer/), [Malvi Bid](https://www.linkedin.com/in/malvibid/), [Raphael Vienne](https://www.linkedin.com/in/raphael-vienne/), [Nenad Aksentijevic](https://www.linkedin.com/in/nenad-aksentijevic-21629a1b6), [Isaac Nicolas](https://www.linkedin.com/in/isaacnicolas/), [Brian Pulfer](https://www.brianpulfer.ch/), [Aldin Cimpo](https://www.linkedin.com/in/aldin-c-b26334189/), [Nikola Ljubesic](https://nljubesi.github.io/)
## Citation
```
@article{serbian-llm-eval,
author = "Gordić Aleksa",
title = "Serbian LLM Eval",
year = "2023"
howpublished = {\url{https://huggingface.co/datasets/gordicaleksa/serbian-llm-eval-v1}},
}
```
## License
Apache 2.0.
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chargoddard/PIPPA-Judged | chargoddard | 2023-12-12T11:14:03Z | 108 | 1 | [
"task_categories:conversational",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"conversational",
"roleplay",
"custom-format",
"region:us"
] | [
"conversational"
] | 2023-08-20T19:59:10Z | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
dataset_info:
- config_name: adequately_rated
features:
- name: id
dtype: string
- name: rating
struct:
- name: analysis
dtype: string
- name: judge
dtype: string
- name: score
dtype: float64
- name: submission_timestamp
dtype: timestamp[ns]
- name: categories
sequence: string
- name: bot_id
dtype: string
- name: bot_name
dtype: string
- name: bot_greeting
dtype: string
- name: bot_definitions
dtype: string
- name: bot_description
dtype: string
- name: conversation
struct:
- name: is_human
sequence: bool
- name: message
sequence: string
splits:
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num_bytes: 203748289.37737644
num_examples: 14610
download_size: 111617678
dataset_size: 203748289.37737644
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splits:
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dataset_size: 10780111.409220532
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splits:
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download_size: 116686573
dataset_size: 234735880
- config_name: ratings_only
features:
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dtype: bool
- name: score
dtype: float64
- name: response
dtype: string
- name: id
dtype: string
splits:
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num_bytes: 7190167
num_examples: 16832
download_size: 2848419
dataset_size: 7190167
configs:
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data_files:
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path: adequately_rated/train-*
- config_name: best_rated
data_files:
- split: train
path: best_rated/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: ratings_only
data_files:
- split: train
path: ratings_only/train-*
---
# Dataset Card for "PIPPA-Judged"
Pygmalion's [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) dataset augmented with quality scores generated by [TheBloke/OpenOrca-Platypus2-13B-GPTQ](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GPTQ).
Making this public so people can reproduce the exact dataset used for one of my models - probably not useful for anything else.
If you want data along these lines, look at Ilya Gusev's [pippa_scored](https://huggingface.co/datasets/IlyaGusev/pippa_scored) instead. It's much higher quality and better executed. | [
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nikgerasimenko/ru_sci_bench | nikgerasimenko | 2023-12-11T17:59:50Z | 108 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-11T16:24:15Z | ---
license: mit
dataset_info:
features:
- name: paper_id
dtype: int64
- name: title
dtype: string
- name: abstract
dtype: string
splits:
- name: ru
num_bytes: 249159304.89138317
num_examples: 194071
- name: en
num_bytes: 234221635.10861683
num_examples: 182436
download_size: 246881206
dataset_size: 483380940.0
---
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librarian-bots/dataset_cards_with_metadata_with_embeddings | librarian-bots | 2023-12-11T10:09:00Z | 107 | 1 | [
"region:us"
] | null | 2023-11-30T11:42:47Z | ---
dataset_info:
features:
- name: datasetId
dtype: string
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: tags
sequence: string
- name: task_categories
sequence: string
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: card
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 376744038.57453775
num_examples: 56424
download_size: 241039915
dataset_size: 376744038.57453775
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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jwl25b/final_project_dataset | jwl25b | 2023-12-17T01:57:40Z | 106 | 0 | [
"region:us"
] | null | 2023-12-17T00:48:16Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3623271.0
num_examples: 50
download_size: 3619422
dataset_size: 3623271.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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hotal/emergency_alpaca | hotal | 2023-12-13T07:22:13Z | 102 | 1 | [
"region:us"
] | null | 2023-12-13T07:15:51Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: system
dtype: string
splits:
- name: train
num_bytes: 26661977
num_examples: 26488
download_size: 2937912
dataset_size: 26661977
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emergency_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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open-llm-leaderboard/results | open-llm-leaderboard | 2023-12-17T03:33:59Z | 101 | 20 | [
"language:en",
"region:us"
] | null | 2023-06-19T15:15:24Z | ---
language:
- en
---

# Open LLM Leaderboard Results
This repository contains the outcomes of your submitted models that have been evaluated through the Open LLM Leaderboard. Our goal is to shed light on the cutting-edge Large Language Models (LLMs) and chatbots, enabling you to make well-informed decisions regarding your chosen application.
## Evaluation Methodology
The evaluation process involves running your models against several benchmarks from the Eleuther AI Harness, a unified framework for measuring the effectiveness of generative language models. Below is a brief overview of each benchmark:
1. AI2 Reasoning Challenge (ARC) - Grade-School Science Questions (25-shot)
2. HellaSwag - Commonsense Inference (10-shot)
3. MMLU - Massive Multi-Task Language Understanding, knowledge on 57 domains (5-shot)
4. TruthfulQA - Propensity to Produce Falsehoods (0-shot)
5. Winogrande - Adversarial Winograd Schema Challenge (5-shot)
6. GSM8k - Grade School Math Word Problems Solving Complex Mathematical Reasoning (5-shot)
Together, these benchmarks provide an assessment of a model's capabilities in terms of knowledge, reasoning, and some math, in various scenarios.
## Exploring Model Details
For further insights into the inputs and outputs of specific models, locate the "📄" emoji associated with the desired model in the leaderboard. Clicking on this icon will direct you to the respective GitHub page containing detailed information about the model's behavior during the evaluation process.
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TeamUNIVA/mathQA | TeamUNIVA | 2023-12-17T18:17:25Z | 97 | 0 | [
"region:us"
] | null | 2023-12-17T11:02:33Z | ---
dataset_info:
features:
- name: question_filename
dtype: string
- name: id
dtype: string
- name: question_text
dtype: string
- name: question_bbox
dtype: string
- name: answer_text
dtype: string
- name: answer_bbox
dtype: string
- name: explanation_filename
dtype: string
- name: explanation_text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: test
num_bytes: 1787424.8960725602
num_examples: 1000
- name: valid
num_bytes: 5968211.727986279
num_examples: 3339
- name: train
num_bytes: 53729992.375941165
num_examples: 30060
download_size: 27179142
dataset_size: 61485629.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: valid
path: data/valid-*
- split: train
path: data/train-*
---
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booksouls/booksum-cleaned | booksouls | 2023-12-15T20:12:35Z | 96 | 0 | [
"region:us"
] | null | 2023-12-15T20:12:27Z | ---
dataset_info:
features:
- name: chapter
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 161588585.20105472
num_examples: 8145
- name: validation
num_bytes: 24977290.21094265
num_examples: 1259
- name: test
num_bytes: 24104374.588002637
num_examples: 1215
download_size: 134806852
dataset_size: 210670250.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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thefrankhsu/hate_speech_twitter | thefrankhsu | 2023-12-15T03:47:33Z | 92 | 1 | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"health",
"tweet",
"hate speech",
"mental health",
"hate speech detection",
"hate speech classification",
"social media",
"mobile health",
"region:us"
] | [
"text-classification"
] | 2023-12-08T04:19:30Z | ---
task_categories:
- text-classification
language:
- en
tags:
- health
- tweet
- hate speech
- mental health
- hate speech detection
- hate speech classification
- social media
- mobile health
size_categories:
- 1K<n<10K
---
## Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
The dataset is designed to analyze and address hate speech within online platforms. It consists of two sets: the training and testing sets. The two datasets have been labeled and categorized instances of hate speech into nine distinct categories.
## Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset comprises three key features: tweets, labels (with hate speech denoted as 1 and non-hate speech as 0), and categories (behavior, class, disability, ethnicity, gender,
physical appearance, race, religion, sexual orientation).
* Training set: contains a total of 5679 tweets (Hate Speech: 1516 / Non Hate Speech: 4163), and the number of hate speech in each category is not equally distributed.
* Testing set: contains a total of 1000 tweets (Hate Speech: 500 / Non Hate Speech: 500), and the number of hate speech in each category is generally even.
## Uses
This dataset can be utilized for various purposes, including but not limited to:
* Developing and training machine learning models for hate speech detection.
* Analyzing the prevalence and patterns of hate speech across different categories.
* Understanding the challenges associated with categorizing hate speech on social media platforms.
Check it out for the example [project](https://github.com/Wei-Hsi/AI4health)!
## Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The dataset utilized in this study is sourced from Kaggle and named the [Hate Speech and Offensive Language dataset](https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offensive-language-dataset/).
Hate speech instances are identified by selecting tweets within the "class" column.
## Annotations
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
Category labels were generated through an OpenAI API call employing the GPT-3.5 model.
It's important to note the instability in category predictions when utilizing GPT-3.5 for label generation, as it tends to predict different categories each time. However, we have confirmed that these tweets were labeled correctly. If there are any misclassified labels, please feel free to reach out. Thank you in advance for your assistance.
## Dataset Card Contact
Please feel free to contact me via [email protected]! | [
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Omnia-S-Abdulnabi/faseeh-testing | Omnia-S-Abdulnabi | 2023-12-12T12:51:14Z | 90 | 0 | [
"region:us"
] | null | 2023-12-12T12:51:14Z | ---
dataset_info:
features:
- name: Arabic
dtype: string
- name: English
dtype: string
splits:
- name: train
num_bytes: 35782.0
num_examples: 101
download_size: 21909
dataset_size: 35782.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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pminervini/inverse-scaling | pminervini | 2023-12-13T20:44:17Z | 89 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T20:30:37Z | ---
license: apache-2.0
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---
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] |
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