datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
jinho8345/funsd | ---
dataset_info:
features:
- name: img
dtype: image
- name: filename
dtype: string
- name: boxes
sequence:
sequence: int64
- name: labels
sequence: string
- name: words
list:
list:
- name: box
sequence: int64
- name: text
dtype: string
- name: linkings
sequence:
sequence:
sequence: int64
- name: ids
sequence: int64
splits:
- name: train
num_bytes: 13690247.0
num_examples: 149
- name: test
num_bytes: 4885049.0
num_examples: 50
download_size: 16731921
dataset_size: 18575296.0
---
# Dataset Card for "funsd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Columbia-NLP__gemma-2b-zephyr-sft | ---
pretty_name: Evaluation run of Columbia-NLP/gemma-2b-zephyr-sft
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Columbia-NLP/gemma-2b-zephyr-sft](https://huggingface.co/Columbia-NLP/gemma-2b-zephyr-sft)\
\ 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 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_Columbia-NLP__gemma-2b-zephyr-sft\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-15T15:03:23.750445](https://huggingface.co/datasets/open-llm-leaderboard/details_Columbia-NLP__gemma-2b-zephyr-sft/blob/main/results_2024-04-15T15-03-23.750445.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\": 0.436223233508748,\n\
\ \"acc_stderr\": 0.03472105889924503,\n \"acc_norm\": 0.44017054709096476,\n\
\ \"acc_norm_stderr\": 0.03548202826314062,\n \"mc1\": 0.27906976744186046,\n\
\ \"mc1_stderr\": 0.015702107090627897,\n \"mc2\": 0.41982937616120947,\n\
\ \"mc2_stderr\": 0.014703089799348862\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4863481228668942,\n \"acc_stderr\": 0.014605943429860947,\n\
\ \"acc_norm\": 0.5127986348122867,\n \"acc_norm_stderr\": 0.014606603181012544\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5419239195379406,\n\
\ \"acc_stderr\": 0.004972210244020563,\n \"acc_norm\": 0.7277434773949413,\n\
\ \"acc_norm_stderr\": 0.00444211526858094\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421296,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421296\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3925925925925926,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.3925925925925926,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.4276315789473684,\n \"acc_stderr\": 0.040260970832965585,\n\
\ \"acc_norm\": 0.4276315789473684,\n \"acc_norm_stderr\": 0.040260970832965585\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5056603773584906,\n \"acc_stderr\": 0.030770900763851316,\n\
\ \"acc_norm\": 0.5056603773584906,\n \"acc_norm_stderr\": 0.030770900763851316\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04148415739394154\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\
: 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n\
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.44508670520231214,\n\
\ \"acc_stderr\": 0.03789401760283647,\n \"acc_norm\": 0.44508670520231214,\n\
\ \"acc_norm_stderr\": 0.03789401760283647\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237656,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237656\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n\
\ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4297872340425532,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.4297872340425532,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2671957671957672,\n \"acc_stderr\": 0.022789673145776568,\n \"\
acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.022789673145776568\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.31746031746031744,\n\
\ \"acc_stderr\": 0.0416345303130286,\n \"acc_norm\": 0.31746031746031744,\n\
\ \"acc_norm_stderr\": 0.0416345303130286\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5161290322580645,\n\
\ \"acc_stderr\": 0.028429203176724555,\n \"acc_norm\": 0.5161290322580645,\n\
\ \"acc_norm_stderr\": 0.028429203176724555\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.37438423645320196,\n \"acc_stderr\": 0.03405155380561952,\n\
\ \"acc_norm\": 0.37438423645320196,\n \"acc_norm_stderr\": 0.03405155380561952\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\
: 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.509090909090909,\n \"acc_stderr\": 0.03903698647748441,\n\
\ \"acc_norm\": 0.509090909090909,\n \"acc_norm_stderr\": 0.03903698647748441\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5050505050505051,\n \"acc_stderr\": 0.035621707606254015,\n \"\
acc_norm\": 0.5050505050505051,\n \"acc_norm_stderr\": 0.035621707606254015\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.5699481865284974,\n \"acc_stderr\": 0.03572954333144808,\n\
\ \"acc_norm\": 0.5699481865284974,\n \"acc_norm_stderr\": 0.03572954333144808\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.38461538461538464,\n \"acc_stderr\": 0.024666744915187222,\n\
\ \"acc_norm\": 0.38461538461538464,\n \"acc_norm_stderr\": 0.024666744915187222\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24444444444444444,\n \"acc_stderr\": 0.02620276653465215,\n \
\ \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.02620276653465215\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.42016806722689076,\n \"acc_stderr\": 0.03206183783236152,\n\
\ \"acc_norm\": 0.42016806722689076,\n \"acc_norm_stderr\": 0.03206183783236152\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.5467889908256881,\n \"acc_stderr\": 0.021343255165546037,\n \"\
acc_norm\": 0.5467889908256881,\n \"acc_norm_stderr\": 0.021343255165546037\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.33796296296296297,\n \"acc_stderr\": 0.03225941352631295,\n \"\
acc_norm\": 0.33796296296296297,\n \"acc_norm_stderr\": 0.03225941352631295\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5294117647058824,\n \"acc_stderr\": 0.03503235296367992,\n \"\
acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03503235296367992\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.5063291139240507,\n \"acc_stderr\": 0.03254462010767859,\n \
\ \"acc_norm\": 0.5063291139240507,\n \"acc_norm_stderr\": 0.03254462010767859\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4170403587443946,\n\
\ \"acc_stderr\": 0.03309266936071721,\n \"acc_norm\": 0.4170403587443946,\n\
\ \"acc_norm_stderr\": 0.03309266936071721\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.45038167938931295,\n \"acc_stderr\": 0.04363643698524779,\n\
\ \"acc_norm\": 0.45038167938931295,\n \"acc_norm_stderr\": 0.04363643698524779\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6198347107438017,\n \"acc_stderr\": 0.04431324501968431,\n \"\
acc_norm\": 0.6198347107438017,\n \"acc_norm_stderr\": 0.04431324501968431\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5185185185185185,\n\
\ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.5185185185185185,\n\
\ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.4662576687116564,\n \"acc_stderr\": 0.039194155450484096,\n\
\ \"acc_norm\": 0.4662576687116564,\n \"acc_norm_stderr\": 0.039194155450484096\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3482142857142857,\n\
\ \"acc_stderr\": 0.045218299028335865,\n \"acc_norm\": 0.3482142857142857,\n\
\ \"acc_norm_stderr\": 0.045218299028335865\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.5048543689320388,\n \"acc_stderr\": 0.04950504382128921,\n\
\ \"acc_norm\": 0.5048543689320388,\n \"acc_norm_stderr\": 0.04950504382128921\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6111111111111112,\n\
\ \"acc_stderr\": 0.031937057262002924,\n \"acc_norm\": 0.6111111111111112,\n\
\ \"acc_norm_stderr\": 0.031937057262002924\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5683269476372924,\n\
\ \"acc_stderr\": 0.017712228939299798,\n \"acc_norm\": 0.5683269476372924,\n\
\ \"acc_norm_stderr\": 0.017712228939299798\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.43352601156069365,\n \"acc_stderr\": 0.026680134761679214,\n\
\ \"acc_norm\": 0.43352601156069365,\n \"acc_norm_stderr\": 0.026680134761679214\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25139664804469275,\n\
\ \"acc_stderr\": 0.014508979453553967,\n \"acc_norm\": 0.25139664804469275,\n\
\ \"acc_norm_stderr\": 0.014508979453553967\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.477124183006536,\n \"acc_stderr\": 0.028599936776089786,\n\
\ \"acc_norm\": 0.477124183006536,\n \"acc_norm_stderr\": 0.028599936776089786\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4437299035369775,\n\
\ \"acc_stderr\": 0.02821768355665231,\n \"acc_norm\": 0.4437299035369775,\n\
\ \"acc_norm_stderr\": 0.02821768355665231\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5030864197530864,\n \"acc_stderr\": 0.02782021415859437,\n\
\ \"acc_norm\": 0.5030864197530864,\n \"acc_norm_stderr\": 0.02782021415859437\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.29432624113475175,\n \"acc_stderr\": 0.02718712701150381,\n \
\ \"acc_norm\": 0.29432624113475175,\n \"acc_norm_stderr\": 0.02718712701150381\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.34810951760104303,\n\
\ \"acc_stderr\": 0.012166738993698205,\n \"acc_norm\": 0.34810951760104303,\n\
\ \"acc_norm_stderr\": 0.012166738993698205\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.39338235294117646,\n \"acc_stderr\": 0.02967428828131118,\n\
\ \"acc_norm\": 0.39338235294117646,\n \"acc_norm_stderr\": 0.02967428828131118\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.3937908496732026,\n \"acc_stderr\": 0.019766211991073052,\n \
\ \"acc_norm\": 0.3937908496732026,\n \"acc_norm_stderr\": 0.019766211991073052\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.509090909090909,\n\
\ \"acc_stderr\": 0.0478833976870286,\n \"acc_norm\": 0.509090909090909,\n\
\ \"acc_norm_stderr\": 0.0478833976870286\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5306122448979592,\n \"acc_stderr\": 0.031949171367580624,\n\
\ \"acc_norm\": 0.5306122448979592,\n \"acc_norm_stderr\": 0.031949171367580624\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.44776119402985076,\n\
\ \"acc_stderr\": 0.035161847729521675,\n \"acc_norm\": 0.44776119402985076,\n\
\ \"acc_norm_stderr\": 0.035161847729521675\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4036144578313253,\n\
\ \"acc_stderr\": 0.038194861407583984,\n \"acc_norm\": 0.4036144578313253,\n\
\ \"acc_norm_stderr\": 0.038194861407583984\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.037867207062342145,\n\
\ \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.037867207062342145\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.27906976744186046,\n\
\ \"mc1_stderr\": 0.015702107090627897,\n \"mc2\": 0.41982937616120947,\n\
\ \"mc2_stderr\": 0.014703089799348862\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.664561957379637,\n \"acc_stderr\": 0.013269575904851413\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.18726307808946172,\n \
\ \"acc_stderr\": 0.010745914199510811\n }\n}\n```"
repo_url: https://huggingface.co/Columbia-NLP/gemma-2b-zephyr-sft
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|arc:challenge|25_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|arc:challenge|25_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|gsm8k|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|gsm8k|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hellaswag|10_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hellaswag|10_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T14-50-03.908318.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T15-03-23.750445.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T15-03-23.750445.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T15-03-23.750445.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- '**/details_harness|winogrande|5_2024-04-15T14-50-03.908318.parquet'
- split: 2024_04_15T15_03_23.750445
path:
- '**/details_harness|winogrande|5_2024-04-15T15-03-23.750445.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-15T15-03-23.750445.parquet'
- config_name: results
data_files:
- split: 2024_04_15T14_50_03.908318
path:
- results_2024-04-15T14-50-03.908318.parquet
- split: 2024_04_15T15_03_23.750445
path:
- results_2024-04-15T15-03-23.750445.parquet
- split: latest
path:
- results_2024-04-15T15-03-23.750445.parquet
---
# Dataset Card for Evaluation run of Columbia-NLP/gemma-2b-zephyr-sft
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Columbia-NLP/gemma-2b-zephyr-sft](https://huggingface.co/Columbia-NLP/gemma-2b-zephyr-sft) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_Columbia-NLP__gemma-2b-zephyr-sft",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-15T15:03:23.750445](https://huggingface.co/datasets/open-llm-leaderboard/details_Columbia-NLP__gemma-2b-zephyr-sft/blob/main/results_2024-04-15T15-03-23.750445.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": 0.436223233508748,
"acc_stderr": 0.03472105889924503,
"acc_norm": 0.44017054709096476,
"acc_norm_stderr": 0.03548202826314062,
"mc1": 0.27906976744186046,
"mc1_stderr": 0.015702107090627897,
"mc2": 0.41982937616120947,
"mc2_stderr": 0.014703089799348862
},
"harness|arc:challenge|25": {
"acc": 0.4863481228668942,
"acc_stderr": 0.014605943429860947,
"acc_norm": 0.5127986348122867,
"acc_norm_stderr": 0.014606603181012544
},
"harness|hellaswag|10": {
"acc": 0.5419239195379406,
"acc_stderr": 0.004972210244020563,
"acc_norm": 0.7277434773949413,
"acc_norm_stderr": 0.00444211526858094
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421296,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421296
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3925925925925926,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.3925925925925926,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4276315789473684,
"acc_stderr": 0.040260970832965585,
"acc_norm": 0.4276315789473684,
"acc_norm_stderr": 0.040260970832965585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5056603773584906,
"acc_stderr": 0.030770900763851316,
"acc_norm": 0.5056603773584906,
"acc_norm_stderr": 0.030770900763851316
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4375,
"acc_stderr": 0.04148415739394154,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04148415739394154
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.44508670520231214,
"acc_stderr": 0.03789401760283647,
"acc_norm": 0.44508670520231214,
"acc_norm_stderr": 0.03789401760283647
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237656,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237656
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.55,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.55,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4297872340425532,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.4297872340425532,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.046446020912223177
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4413793103448276,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.4413793103448276,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2671957671957672,
"acc_stderr": 0.022789673145776568,
"acc_norm": 0.2671957671957672,
"acc_norm_stderr": 0.022789673145776568
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.31746031746031744,
"acc_stderr": 0.0416345303130286,
"acc_norm": 0.31746031746031744,
"acc_norm_stderr": 0.0416345303130286
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5161290322580645,
"acc_stderr": 0.028429203176724555,
"acc_norm": 0.5161290322580645,
"acc_norm_stderr": 0.028429203176724555
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.37438423645320196,
"acc_stderr": 0.03405155380561952,
"acc_norm": 0.37438423645320196,
"acc_norm_stderr": 0.03405155380561952
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.509090909090909,
"acc_stderr": 0.03903698647748441,
"acc_norm": 0.509090909090909,
"acc_norm_stderr": 0.03903698647748441
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5050505050505051,
"acc_stderr": 0.035621707606254015,
"acc_norm": 0.5050505050505051,
"acc_norm_stderr": 0.035621707606254015
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.5699481865284974,
"acc_stderr": 0.03572954333144808,
"acc_norm": 0.5699481865284974,
"acc_norm_stderr": 0.03572954333144808
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.38461538461538464,
"acc_stderr": 0.024666744915187222,
"acc_norm": 0.38461538461538464,
"acc_norm_stderr": 0.024666744915187222
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.24444444444444444,
"acc_stderr": 0.02620276653465215,
"acc_norm": 0.24444444444444444,
"acc_norm_stderr": 0.02620276653465215
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.42016806722689076,
"acc_stderr": 0.03206183783236152,
"acc_norm": 0.42016806722689076,
"acc_norm_stderr": 0.03206183783236152
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33112582781456956,
"acc_stderr": 0.038425817186598696,
"acc_norm": 0.33112582781456956,
"acc_norm_stderr": 0.038425817186598696
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5467889908256881,
"acc_stderr": 0.021343255165546037,
"acc_norm": 0.5467889908256881,
"acc_norm_stderr": 0.021343255165546037
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.33796296296296297,
"acc_stderr": 0.03225941352631295,
"acc_norm": 0.33796296296296297,
"acc_norm_stderr": 0.03225941352631295
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5294117647058824,
"acc_stderr": 0.03503235296367992,
"acc_norm": 0.5294117647058824,
"acc_norm_stderr": 0.03503235296367992
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.5063291139240507,
"acc_stderr": 0.03254462010767859,
"acc_norm": 0.5063291139240507,
"acc_norm_stderr": 0.03254462010767859
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.4170403587443946,
"acc_stderr": 0.03309266936071721,
"acc_norm": 0.4170403587443946,
"acc_norm_stderr": 0.03309266936071721
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.45038167938931295,
"acc_stderr": 0.04363643698524779,
"acc_norm": 0.45038167938931295,
"acc_norm_stderr": 0.04363643698524779
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6198347107438017,
"acc_stderr": 0.04431324501968431,
"acc_norm": 0.6198347107438017,
"acc_norm_stderr": 0.04431324501968431
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.04830366024635331,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.04830366024635331
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.4662576687116564,
"acc_stderr": 0.039194155450484096,
"acc_norm": 0.4662576687116564,
"acc_norm_stderr": 0.039194155450484096
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.3482142857142857,
"acc_stderr": 0.045218299028335865,
"acc_norm": 0.3482142857142857,
"acc_norm_stderr": 0.045218299028335865
},
"harness|hendrycksTest-management|5": {
"acc": 0.5048543689320388,
"acc_stderr": 0.04950504382128921,
"acc_norm": 0.5048543689320388,
"acc_norm_stderr": 0.04950504382128921
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.6111111111111112,
"acc_stderr": 0.031937057262002924,
"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.031937057262002924
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5683269476372924,
"acc_stderr": 0.017712228939299798,
"acc_norm": 0.5683269476372924,
"acc_norm_stderr": 0.017712228939299798
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.43352601156069365,
"acc_stderr": 0.026680134761679214,
"acc_norm": 0.43352601156069365,
"acc_norm_stderr": 0.026680134761679214
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.25139664804469275,
"acc_stderr": 0.014508979453553967,
"acc_norm": 0.25139664804469275,
"acc_norm_stderr": 0.014508979453553967
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.477124183006536,
"acc_stderr": 0.028599936776089786,
"acc_norm": 0.477124183006536,
"acc_norm_stderr": 0.028599936776089786
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.4437299035369775,
"acc_stderr": 0.02821768355665231,
"acc_norm": 0.4437299035369775,
"acc_norm_stderr": 0.02821768355665231
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5030864197530864,
"acc_stderr": 0.02782021415859437,
"acc_norm": 0.5030864197530864,
"acc_norm_stderr": 0.02782021415859437
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.29432624113475175,
"acc_stderr": 0.02718712701150381,
"acc_norm": 0.29432624113475175,
"acc_norm_stderr": 0.02718712701150381
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.34810951760104303,
"acc_stderr": 0.012166738993698205,
"acc_norm": 0.34810951760104303,
"acc_norm_stderr": 0.012166738993698205
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.39338235294117646,
"acc_stderr": 0.02967428828131118,
"acc_norm": 0.39338235294117646,
"acc_norm_stderr": 0.02967428828131118
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.3937908496732026,
"acc_stderr": 0.019766211991073052,
"acc_norm": 0.3937908496732026,
"acc_norm_stderr": 0.019766211991073052
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.509090909090909,
"acc_stderr": 0.0478833976870286,
"acc_norm": 0.509090909090909,
"acc_norm_stderr": 0.0478833976870286
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5306122448979592,
"acc_stderr": 0.031949171367580624,
"acc_norm": 0.5306122448979592,
"acc_norm_stderr": 0.031949171367580624
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.44776119402985076,
"acc_stderr": 0.035161847729521675,
"acc_norm": 0.44776119402985076,
"acc_norm_stderr": 0.035161847729521675
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4036144578313253,
"acc_stderr": 0.038194861407583984,
"acc_norm": 0.4036144578313253,
"acc_norm_stderr": 0.038194861407583984
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5789473684210527,
"acc_stderr": 0.037867207062342145,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.037867207062342145
},
"harness|truthfulqa:mc|0": {
"mc1": 0.27906976744186046,
"mc1_stderr": 0.015702107090627897,
"mc2": 0.41982937616120947,
"mc2_stderr": 0.014703089799348862
},
"harness|winogrande|5": {
"acc": 0.664561957379637,
"acc_stderr": 0.013269575904851413
},
"harness|gsm8k|5": {
"acc": 0.18726307808946172,
"acc_stderr": 0.010745914199510811
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
TeetouchQQ/test_data | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
- name: raw_entities
struct:
- name: EMAIL
sequence: 'null'
- name: ID_NUM
sequence: string
- name: NAME_STUDENT
sequence: string
- name: PHONE_NUM
sequence: string
- name: STREET_ADDRESS
sequence: string
- name: URL_PERSONAL
sequence: string
- name: USERNAME
sequence: 'null'
- name: id
dtype: string
splits:
- name: train
num_bytes: 9707911
num_examples: 1022
download_size: 4581984
dataset_size: 9707911
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
shrikant11/myra | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 292896.0
num_examples: 6
download_size: 293727
dataset_size: 292896.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xfh/lexica_6k | ---
dataset_info:
features:
- name: text
dtype: string
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: md5
dtype: string
- name: tag
dtype: string
splits:
- name: train
num_examples: 12048
---
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
camilo4bai/bonito_privacy_qa_sft_data_t4 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2290
num_examples: 8
- name: test
num_bytes: 714
num_examples: 2
download_size: 8857
dataset_size: 3004
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
hanifsyarubany10/FreedomIntelligence-indo-gemma | ---
dataset_info:
features:
- name: context
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
- name: instruction_source
dtype: string
- name: prompt
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 138106237
num_examples: 49969
download_size: 63118744
dataset_size: 138106237
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cdleong/piglatin-mt | ---
language:
- en
license:
- mit
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
language_details: eng and engyay
---
## Dataset Description
- **Homepage:** cdleong.github.io
# Dataset Summary:
Pig-latin machine and English parallel machine translation corpus.
Based on [The Project Gutenberg EBook of "De Bello Gallico" and Other Commentaries](https://www.gutenberg.org/ebooks/10657)
Converted to pig-latin with https://github.com/bpabel/piglatin
Blank lines removed.
## Dataset Structure
```
DatasetDict({
train: Dataset({
features: ['translation'],
num_rows: 14778
})
validation: Dataset({
features: ['translation'],
num_rows: 1000
})
})
```
### Data Instances
```
{
'translation':
{
'eng': 'thrown into disorder they returned with more precipitation than is usual',
'engyay': 'own-thray into-ay isorder-day ey-thay eturned-ray ith-way ore-may ecipitation-pray an-thay is-ay usual-ay'
}
}
```
### Data Fields
- `translation`: a dictionary containing two strings paired with a key indicating the corresponding language.
### Data Splits
- `train`: most of the data, 13,232 samples total.
- `dev`: 1k holdout samples, created with the datasets.train_test_split() function |
cellowmaia/AudioAntonio | ---
license: openrail
---
|
Henu-Software/Henu-MultiSubjects | ---
license: cc-by-nc-4.0
---
|
yjching/tokenized_ts_data | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Problem
dtype: string
- name: Resolution
dtype: string
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1272561
num_examples: 197
download_size: 78711
dataset_size: 1272561
---
# Dataset Card for "tokenized_ts_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713207994 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 31506
num_examples: 81
download_size: 25652
dataset_size: 31506
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713207994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hle2000/Mintaka_Graph_Features_T5-large-ssm | ---
dataset_info:
features:
- name: question
dtype: string
- name: question_answer
dtype: string
- name: num_nodes
dtype: int64
- name: num_edges
dtype: int64
- name: density
dtype: float64
- name: cycle
dtype: int64
- name: bridge
dtype: int64
- name: katz_centrality
dtype: float64
- name: page_rank
dtype: float64
- name: avg_ssp_length
dtype: float64
- name: graph_sequence
dtype: string
- name: updated_graph_sequence
dtype: string
- name: graph_sequence_embedding
dtype: string
- name: updated_graph_sequence_embedding
dtype: string
- name: question_answer_embedding
dtype: string
- name: tfidf_vector
dtype: string
- name: correct
dtype: float64
splits:
- name: train
num_bytes: 2359468579
num_examples: 22772
- name: test
num_bytes: 2359468579
num_examples: 22772
download_size: 864051150
dataset_size: 4718937158
---
# Dataset Card for "Mintaka_Graph_Features_T5-large-ssm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pssubitha/formatted_data_sales1.jsonl | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 45883
num_examples: 120
download_size: 24605
dataset_size: 45883
---
# Dataset Card for "formatted_data_sales1.jsonl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sankettgorey/donut_6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 350410662.6
num_examples: 800
- name: test
num_bytes: 43730265.7
num_examples: 100
- name: valid
num_bytes: 43819720.7
num_examples: 100
download_size: 402661296
dataset_size: 437960649.0
---
# Dataset Card for "donut_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ziozzang/osx_dictionary_translation_pairs | ---
task_categories:
- translation
language:
- ko
- en
- cs
- ar
- nl
- fi
- fr
- de
- hu
- hi
- el
- pl
- id
- it
- pt
- ru
- vi
- tr
- te
- es
- zh
- th
- ja
---
Apple's Internal dictionary extracted.
- the pairs are word level example of translation pairs (usage case, or example pairs)
- Original data are Human curated.
- This can be used for make machine generated training data.
License
- I have no claim of license.
Expected Usecase
- This dataset is for simple test, tasks for translation case.
---
Pipeline example
- feed as example. and LLM can generate translation pairs to better translation.
References
- apple-peeler: https://pypi.org/project/apple-peeler/
|
huggingartists/veggietales | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/veggietales"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.220878 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d14c9e27b39f0e250784a2dce037a03d.720x720x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/veggietales">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">VeggieTales</div>
<a href="https://genius.com/artists/veggietales">
<div style="text-align: center; font-size: 14px;">@veggietales</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/veggietales).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/veggietales")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|163| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/veggietales")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
result-kand2-sdxl-wuerst-karlo/53f478ab | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 257
num_examples: 10
download_size: 1433
dataset_size: 257
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "53f478ab"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
adamo1139/PS_AD_Office_01 | ---
license: unknown
---
Synthetic dataset of PowerShell, Active Directory and I think some Office 365 Q&A |
kaleemWaheed/twitter_dataset_1713204962 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 26372
num_examples: 62
download_size: 15551
dataset_size: 26372
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
princeton-nlp/QuRatedPajama-1B_tokens_for_analysis | ---
pretty_name: QuRatedPajama-1B_tokens_for_analysis
---
## QuRatedPajama
**Paper:** [QuRating: Selecting High-Quality Data for Training Language Models](https://arxiv.org/pdf/2402.09739.pdf)
This dataset is a 1B token subset derived from [princeton-nlp/QuRatedPajama-260B](https://huggingface.co/datasets/princeton-nlp/QuRatedPajama-260B), which is a subset of [cerebras/SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B) annotated by [princeton-nlp/QuRater-1.3B](https://huggingface.co/princeton-nlp/QuRater-1.3B) with sequence-level quality ratings across 4 criteria:
- **Educational Value** - e.g. the text includes clear explanations, step-by-step reasoning, or questions and answers
- **Facts & Trivia** - how much factual and trivia knowledge the text contains, where specific facts and obscure trivia are preferred over more common knowledge
- **Writing Style** - how polished and good is the writing style in the text
- **Required Expertise**: - how much required expertise and prerequisite knowledge is necessary to understand the text
This subset is useful for analysis of quality ratings. It unsupervised domain clusters for the CommonCrawl and C4 domains (a description of these clusters can be found [here](https://huggingface.co/datasets/princeton-nlp/QuRatedPajama-1B_tokens_for_analysis/blob/main/cluster_checkpoint-1M_docs_for_analysis-k25/top_terms_with_title.csv)). We also report the quality ratings per 512 token chunk of each example.
In a pre-processing step, we split documents in into chunks of exactly 1024 tokens. We provide tokenization with the Llama-2 tokenizer in the `input_ids` column.
**Guidance on Responsible Use:**
In the paper, we document various types of bias that are present in the quality ratings (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper).
Hence, be aware that data selection with QuRating could have unintended and harmful effects on the language model that is being trained. We strongly recommend a comprehensive evaluation of the language model for these and other types of bias, particularly before real-world deployment. We hope that releasing the data/models can facilitate future research aimed at uncovering and mitigating such biases.
**Citation:**
```
@article{wettig2024qurating,
title={QuRating: Selecting High-Quality Data for Training Language Models},
author={Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen},
journal={arXiv preprint 2402.09739},
year={2024}
}
``` |
ybelkada/common_voice_mr_11_0_copy | ---
dataset_info:
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
splits:
- name: train
num_bytes: 81761699.0
num_examples: 2245
- name: validation
num_bytes: 65082681.0
num_examples: 1682
- name: test
num_bytes: 69247449.0
num_examples: 1816
- name: other
num_bytes: 109682091.0
num_examples: 2819
- name: invalidated
num_bytes: 90463060.0
num_examples: 2237
download_size: 407562763
dataset_size: 416236980.0
---
# Dataset Card for "common_voice_mr_11_0_copy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
thercyl/ADBE | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: float64
- name: Ticker
dtype: string
- name: Year
dtype: string
- name: Text
dtype: string
- name: Embedding
dtype: string
splits:
- name: train
num_bytes: 39936377
num_examples: 1143
download_size: 23884734
dataset_size: 39936377
---
# Dataset Card for "thercyl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
when2rl/UltraFeedback_binarized_cleaned_annotated | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: other_info
struct:
- name: chosen_annotations
struct:
- name: annotations
struct:
- name: helpfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: honesty
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: instruction_following
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: truthfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: critique
dtype: string
- name: fine_grained_score
dtype: float64
- name: model
dtype: string
- name: overall_score
dtype: float64
- name: correct_answers
sequence: string
- name: incorrect_answers
sequence: string
- name: rejected_annotations
struct:
- name: annotations
struct:
- name: helpfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: honesty
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: instruction_following
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: truthfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: critique
dtype: string
- name: fine_grained_score
dtype: float64
- name: model
dtype: string
- name: overall_score
dtype: float64
- name: source
dtype: string
splits:
- name: train_prefs
num_bytes: 614823879
num_examples: 61135
- name: test_prefs
num_bytes: 20002694
num_examples: 2000
download_size: 328657992
dataset_size: 634826573
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
---
# Dataset Card for UltraFeedback Binarized, Cleaned, and Annotated
<!-- Provide a quick summary of the dataset. -->
This basically comes from:
1. start from UltraFeedback Binarized
2. recover metadata information such as `source` and `annotations` by matching prompts from the original `UltraFeedback` dataset
3. augment the original dset with metadata information stored in `other_info`
## Dataset Details
Same usage as `HuggingFaceH4/ultrafeedback_binarized`, but added the `other_info` which contains information such as `source` and `annotations`.
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
katielink/gpt4_bias | ---
license: unknown
tags:
- medical
configs:
- config_name: nursing_bias
data_files: "data/nursing_bias/unconscious_bias_nurses_final.csv"
default: true
- config_name: healer_cases_ED_cases
data_files: "data/healer_cases/ED_cases/ED_cases.csv"
- config_name: healer_cases_chest_pain_outputs
data_files: "data/healer_cases/chest_pain/Outpt_chest_pain.csv"
- config_name: healer_cases_dyspnea_outputs
data_files: "data/healer_cases/dyspnea/Outpt_dyspnea.csv"
- config_name: healer_cases_pharyngitis_outputs
data_files: "data/healer_cases/DDx_pharyngitis_Figure_2/pharyngitis.csv"
---
# Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in Healthcare
This repository accompanies the paper ["Coding Inequity: Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in Healthcare"](https://www.medrxiv.org/content/10.1101/2023.07.13.23292577v1).
## Overview
The data is available in the `data_to_share` folder. This can be broken into several pieces:
1. `simulated_pt_distribution` --- here is where we store all the information for generating patient demographic distributions. We store the outputs of GPT-4, as well as the true prevelence distribution.
2. `nursing_bias` --- this is where the transformed nursing bias cases are stored. We additionally store the outputs here.
3. `healer_cases` --- this is where the healer cases are stored. We additionally store the outputs here.
### Demographic Distribution
There are two folders in `simulated_pt_distribution` --- `outputs` and `true_dist_work`. In `outputs`, the files are just outputs of GPT-4. These are all pickle files. You can load these by running the following commands:
```
import pickle
PATH_TO_PICKLE_FILE = "data_to_share/simulated_pt_distribution/outputs/Bacterial Pneumonia_GPT4_x50.pkl"
with open(PATH_TO_PICKLE_FILE, "rb") as f:
loaded_file = pickle.load(f)
```
To see the the true distributions, as well as which sources they came from, please look at `final_true_dist.csv`. There are some other CSVs in this folder; however, `final_true_dist.csv` is the main file that should be looked at. The other two important ones are `true_prevelence_potentially_unormalized_conditionals.csv` and `true_prevelence_potentially_unormalized.csv`, which have
additional information about where the sources came from, as well as the conditional probabilities of the conditions.
### Nursing Bias Cases
This folder mostly contains the vignettes, as well as the outputs of GPT-4. The vignettes can either by loaded through the .py files OR through the csv file. To load the CSV file, you can use the following code:
```
import pandas as pd
df = pd.read_csv("data_to_share/nursing_bias/unconscious_bias_nurses_final.csv")
```
The CSV has the following keys: `case`, `gender`, `race`, `text`, `system`, `prompt`, `options`.
- `case`: Which of the vignettes does it belong to?
- `gender`: Which gender is discussed in the `text`?
- `race`: Which race is discussed in the `text`?
- `text`: The vignette filled in with `gender` and `race`.
- `system`: What is the system level prompt we should use for GPT-4.
- `prompt`: Everything that should be passed to GPT-4. It has `text` and `options`.
- `options`: What are the possible options
### Healer Cases
Unfortunately, this is the messiest part of the data --- We apologize in advance! The key things to know is that the CSV files contain the original healer prompts and data, while the PKL files contain the outputs. The CSV files have the following rows:
- `title`: The title of the case. This will be essential for matching it to the output in the PKLs.
- `Case one liner`: The actual case we provide GPT-4.
- `DDx`: A list of potential ddxs --- you will need to split by newlines.
We additionally provide the outputs of GPT-4 for each of these cases. These can be found in the PKL files.
### Prompts
This folder has some basic prompts that we use throughout the code.
## Running Code
The code can be found in the github repository: https://github.com/elehman16/gpt4_bias
In this section, we will describe the code layout! This is still a work in progress. If you are re-running OpenAI commands, be sure to set the `os.environ` properly, in order to contain your specific API key.
### Preprocessing
To generate the nursing bias cases from the `.py` files, please see this script here: `preprocessing/create_unconscious_bias_cases.py`. This will allow you to generate the CSV found at `data_to_share/nursing_bias/unconscious_bias_nurses_final.csv`.
### GPT-4 Outputs
A lot of the code for generating the outputs of GPT-4 can be found in the `src/notebooks` file. However, for a basic understanding of how we do this, I would recommend looking at `get_gpt4_dist.py`, which queries for the conditions seen in Figure 1.
### Running Code
The code to generate the figures can be seen in either their respective folder (e.g., `src/healer_cases/`) or in `src/notebooks`. Most of these scripts assume that you have already preprocessed the data, and have run it through GPT-4.
## Questions
If you have questions, please email `lehmer16@mit.edu` or raise an issue on the Github. |
CyberHarem/chihiro_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of chihiro/各務チヒロ/千寻 (Blue Archive)
This is the dataset of chihiro/各務チヒロ/千寻 (Blue Archive), containing 348 images and their tags.
The core tags of this character are `short_hair, glasses, halo, black_hair, hair_ornament, breasts, semi-rimless_eyewear, large_breasts, green_eyes, rabbit_hair_ornament, hair_between_eyes, blue_hair, blue-framed_eyewear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 348 | 609.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chihiro_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 348 | 507.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chihiro_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 884 | 1.04 GiB | [Download](https://huggingface.co/datasets/CyberHarem/chihiro_bluearchive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/chihiro_bluearchive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, blue_necktie, collared_shirt, simple_background, solo, upper_body, white_shirt, looking_at_viewer, white_background, closed_mouth, blush, long_sleeves, id_card, open_jacket, under-rim_eyewear, two-tone_jacket |
| 1 | 11 |  |  |  |  |  | 1girl, blue_cardigan, blue_necktie, long_sleeves, solo, blush, closed_mouth, collared_shirt, looking_at_viewer, open_jacket, two-tone_jacket, white_shirt, black_skirt, canned_coffee, holding_can, pleated_skirt, id_card, wristwatch, cowboy_shot, drink_can, outdoors, smile |
| 2 | 8 |  |  |  |  |  | 1girl, black_skirt, blue_cardigan, blue_necktie, collared_shirt, id_card, long_sleeves, open_jacket, pleated_skirt, solo, white_shirt, looking_at_viewer, cowboy_shot, two-tone_jacket, wristwatch, closed_mouth, blue_sweater_vest, simple_background, white_background |
| 3 | 5 |  |  |  |  |  | 1girl, alternate_costume, black_shirt, blue_jacket, simple_background, solo, track_jacket, white_background, gym_uniform, long_sleeves, looking_at_viewer, blush, closed_mouth, black_buruma, blue_buruma, cowboy_shot, open_clothes, partially_unzipped, short_sleeves, sweat, thighs |
| 4 | 13 |  |  |  |  |  | 1girl, cleavage, millennium_cheerleader_outfit_(blue_archive), white_skirt, navel, pleated_skirt, blush, cosplay, midriff, solo, holding_pom_poms, miniskirt, bare_shoulders, closed_mouth, looking_at_viewer, sweat, simple_background, stomach, crop_top, detached_collar, white_background |
| 5 | 19 |  |  |  |  |  | 1girl, solo, collarbone, blush, closed_mouth, looking_at_viewer, navel, cleavage, stomach, bare_shoulders, bikini, simple_background, alternate_costume, white_background |
| 6 | 14 |  |  |  |  |  | 1girl, blush, hetero, 1boy, nipples, sex, open_mouth, solo_focus, vaginal, mosaic_censoring, navel, penis, blue_necktie, open_clothes, white_shirt, female_pubic_hair, pussy, collarbone, long_sleeves, sweat, completely_nude, cowgirl_position, girl_on_top |
| 7 | 9 |  |  |  |  |  | long_sleeves, 1girl, black_gloves, blue_headwear, closed_mouth, hat, looking_at_viewer, black_pantyhose, smile, solo, hood, multicolored_jacket, backpack, blush, official_alternate_costume, black_footwear, black_skirt, holding, shoes, simple_background |
| 8 | 15 |  |  |  |  |  | 1girl, alternate_costume, cleavage, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, strapless_leotard, solo, bare_shoulders, blush, simple_background, white_background, wrist_cuffs, covered_navel, highleg_leotard, blue_leotard, closed_mouth, black_leotard, black_pantyhose, blue_bowtie, rabbit_tail, blue_necktie, fake_tail, jacket, open_clothes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_necktie | collared_shirt | simple_background | solo | upper_body | white_shirt | looking_at_viewer | white_background | closed_mouth | blush | long_sleeves | id_card | open_jacket | under-rim_eyewear | two-tone_jacket | blue_cardigan | black_skirt | canned_coffee | holding_can | pleated_skirt | wristwatch | cowboy_shot | drink_can | outdoors | smile | blue_sweater_vest | alternate_costume | black_shirt | blue_jacket | track_jacket | gym_uniform | black_buruma | blue_buruma | open_clothes | partially_unzipped | short_sleeves | sweat | thighs | cleavage | millennium_cheerleader_outfit_(blue_archive) | white_skirt | navel | cosplay | midriff | holding_pom_poms | miniskirt | bare_shoulders | stomach | crop_top | detached_collar | collarbone | bikini | hetero | 1boy | nipples | sex | open_mouth | solo_focus | vaginal | mosaic_censoring | penis | female_pubic_hair | pussy | completely_nude | cowgirl_position | girl_on_top | black_gloves | blue_headwear | hat | black_pantyhose | hood | multicolored_jacket | backpack | official_alternate_costume | black_footwear | holding | shoes | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | covered_navel | highleg_leotard | blue_leotard | black_leotard | blue_bowtie | rabbit_tail | fake_tail | jacket |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------------|:--------------------|:-------|:-------------|:--------------|:--------------------|:-------------------|:---------------|:--------|:---------------|:----------|:--------------|:--------------------|:------------------|:----------------|:--------------|:----------------|:--------------|:----------------|:-------------|:--------------|:------------|:-----------|:--------|:--------------------|:--------------------|:--------------|:--------------|:---------------|:--------------|:---------------|:--------------|:---------------|:---------------------|:----------------|:--------|:---------|:-----------|:-----------------------------------------------|:--------------|:--------|:----------|:----------|:-------------------|:------------|:-----------------|:----------|:-----------|:------------------|:-------------|:---------|:---------|:-------|:----------|:------|:-------------|:-------------|:----------|:-------------------|:--------|:--------------------|:--------|:------------------|:-------------------|:--------------|:---------------|:----------------|:------|:------------------|:-------|:----------------------|:-----------|:-----------------------------|:-----------------|:----------|:--------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:----------------|:------------------|:---------------|:----------------|:--------------|:--------------|:------------|:---------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | | X | | X | X | | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | X | | X | X | X | X | | X | X | X | | X | X | X | | | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | X | X | | | X | X | X | X | X | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 13 |  |  |  |  |  | X | | | X | X | | | X | X | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 19 |  |  |  |  |  | X | | | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | X | | | | | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 14 |  |  |  |  |  | X | X | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | X | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 9 |  |  |  |  |  | X | | | X | X | | | X | | X | X | X | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 8 | 15 |  |  |  |  |  | X | X | | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | X | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Nexdata/155_Hours_Lip_Sync_Multimodal_Video_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
Voice and matching lip language video filmed with 250 people by multi-devices simultaneously, aligned precisely by pulse signal, with high accuracy. It can be used in multi-modal learning algorithms research in speech and image fields.
For more details, please refer to the link: https://www.nexdata.ai/dataset/996?source=Huggingface
## Format
Video: mp4 format, 1,280*720, Audio: wav format, 16HZ, 16bit mono
## Recording Environment
Using quiet sunny room to stimulate daytime outdoor driving scenes,Signal to noise ratio 25~20dB
## Recording Scenes
divide to big scenes and sub scenes by different intense of sunlight
## Recording Content
Short signals and spoken sentences
## Recording People
250 Chinese, balance for gender
## Recording Device
Camera, HD microphone, Audio board
## Recording angle
Recording videos of front face, single side face, looking up, looking down, side face looking down and side face looking up all 6 different angles, and proximal and distant audio at the same time
## Language
Mandarin
## Application scenario
Lip Language recognization
## Accuracy
Accuracy of sentence should not below 95%
# Licensing Information
Commercial License
|
hlt-lab/dailydialogsample-expansions | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 16604
num_examples: 36
download_size: 17256
dataset_size: 16604
---
# Dataset Card for "dailydialogsample-expansions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Yijia-Xiao/PPLM-PQA | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: cleaned_output
dtype: string
splits:
- name: train
num_bytes: 8673197
num_examples: 42499
- name: test
num_bytes: 1536768
num_examples: 7504
download_size: 1233735
dataset_size: 10209965
---
# Dataset Card for "PPLM-PQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/i_26_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of i_26/伊26/伊26 (Kantai Collection)
This is the dataset of i_26/伊26/伊26 (Kantai Collection), containing 46 images and their tags.
The core tags of this character are `hairband, light_brown_hair, long_hair, two_side_up, breasts, two-tone_hairband, brown_eyes, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 46 | 70.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_26_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 46 | 35.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_26_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 122 | 86.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_26_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 46 | 62.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_26_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 122 | 137.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/i_26_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/i_26_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 46 |  |  |  |  |  | 1girl, solo, one-piece_swimsuit, looking_at_viewer, new_school_swimsuit, smile, short_sleeves, open_mouth, sailor_collar, swimsuit_under_clothes, blush, name_tag, collarbone, open_clothes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | one-piece_swimsuit | looking_at_viewer | new_school_swimsuit | smile | short_sleeves | open_mouth | sailor_collar | swimsuit_under_clothes | blush | name_tag | collarbone | open_clothes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------------|:--------------------|:----------------------|:--------|:----------------|:-------------|:----------------|:-------------------------|:--------|:-----------|:-------------|:---------------|
| 0 | 46 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
pphuc25/vlsp2023-test2 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: path
dtype: string
splits:
- name: train
num_bytes: 6248152210.804
num_examples: 54874
download_size: 6346575989
dataset_size: 6248152210.804
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vlsp2023-test2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ammok/media_campaign_cost | ---
license: mit
---
---
configs:
- config_name: train
data_files: "train.csv"
sep: "\t"
- config_name: test
data_files: "test.csv"
sep: ","
--- |
hudssntao/test_dataset | ---
dataset_info:
features:
- name: column1
dtype: string
- name: column2
dtype: string
splits:
- name: train
num_bytes: 40
num_examples: 2
download_size: 1227
dataset_size: 40
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "test_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nateraw/video-demo | ---
license: mit
---
|
AdapterOcean/augmentatio-standardized_cluster_8 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 35731704
num_examples: 3270
download_size: 10632958
dataset_size: 35731704
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "augmentatio-standardized_cluster_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
enoahjr/twitter_dataset_1713229544 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 281770
num_examples: 802
download_size: 133306
dataset_size: 281770
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/103_Hours_Indonesian_Spontaneous_Dialogue_Smartphone_Speech_Dataset | ---
license: cc-by-nc-nd-4.0
---
## Description
Indonesian(Indonesia) Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics, covering 20+ domains. Transcribed with text content, speaker's ID, gender, age and other attributes. Our dataset was collected from extensive and diversify speakers(168 native speakers), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1447?source=Huggingface
## Format
16k Hz, 16 bit, wav, mono channel;
## Content category
Dialogue based on given topics;
## Recording condition
Low background noise (indoor);
## Recording device
Android smartphone, iPhone;
## Speaker
412 native speakers in total, 55% male and 45% female;
## Country
Indonesia(IDN);
## Language(Region) Code
id-ID;
## Language
Indonesian;
## Features of annotation
Transcription text, timestamp, speaker ID, gender,PII redacted.
## Accuracy Rate
Word Accuracy Rate (WAR) 98%
# Licensing Information
Commercial License
|
result-kand2-sdxl-wuerst-karlo/d50de234 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1368
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "d50de234"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
weqweasdas/rsf_pi0_mistrav_02_prompt0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: type
dtype: string
- name: instances
list:
- name: old_prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: responses
sequence: string
splits:
- name: train
num_bytes: 223456113
num_examples: 1
download_size: 103712387
dataset_size: 223456113
---
# Dataset Card for "rsf_pi0_mistrav_02_prompt0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ravithejads/alpaca-cleaned-tagged | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 42276800
num_examples: 51760
download_size: 24347133
dataset_size: 42276800
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ouvic215/Soldering-Data-pix2pix-1022-white | ---
dataset_info:
features:
- name: mask_image
dtype: image
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 1579248414.25
num_examples: 19151
download_size: 1217691208
dataset_size: 1579248414.25
---
# Dataset Card for "Soldering-Data-pix2pix-1022-white"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
michaelmallari/airbnb-ca-mb-winnipeg | ---
license: mit
---
|
tanish001/guanaco-llama2-1k | ---
dataset_info:
features:
- name: train
dtype: string
splits:
- name: train
num_bytes: 3127215
num_examples: 3512
download_size: 1669314
dataset_size: 3127215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
joey234/mmlu-moral_disputes-dev | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 3132
num_examples: 5
download_size: 6906
dataset_size: 3132
---
# Dataset Card for "mmlu-moral_disputes-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Intuit-GenSRF/all_english_datasets | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
- name: encoded_labels
sequence: int64
- name: lang
dtype: string
- name: has_toxic
dtype: int64
- name: has_profane
dtype: int64
- name: has_insult
dtype: int64
- name: has_hate
dtype: int64
- name: has_threat
dtype: int64
- name: has_sexual
dtype: int64
- name: has_offensive
dtype: int64
- name: has_selfharm
dtype: int64
- name: has_harassment
dtype: int64
splits:
- name: train
num_bytes: 1498751715
num_examples: 2921884
download_size: 616223055
dataset_size: 1498751715
---
# Dataset Card for "all_english_datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xzuyn/beavertails-alpaca | ---
size_categories:
- 100K<n<1M
---
# Original Dataset: [BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails)
```json
{
'Animal Abuse': {
True: 3480,
False: 297087
},
'Child Abuse': {
True: 1664,
False: 298903
},
'Controversial Topics, Politics': {
True: 9233,
False: 291334
},
'Discrimination, Stereotype, Injustice': {
True: 24006,
False: 276561
},
'Drug Abuse, Weapons, Banned Substance': {
True: 16724,
False: 283843
},
'Financial Crime, Property Crime, Theft': {
True: 28769,
False: 271798
},
'Hate Speech, Offensive Language': {
True: 27127,
False: 273440
},
'Misinformation Regarding Ethics, Laws And Safety': {
True: 3835,
False: 296732
},
'Non Violent Unethical Behavior': {
True: 59992,
False: 240575
},
'Privacy Violation': {
True: 14774,
False: 285793
},
'Self Harm': {
True: 2024,
False: 298543
},
'Sexually Explicit, Adult Content': {
True: 6876,
False: 293691
},
'Terrorism, Organized Crime': {
True: 2457,
False: 298110
},
'Violence, Aiding And Abetting, Incitement': {
True: 79544,
False: 221023
}
}
```
# Paper: [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://arxiv.org/abs/2307.04657)
```
@article{beavertails,
title = {BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset},
author = {Jiaming Ji and Mickel Liu and Juntao Dai and Xuehai Pan and Chi Zhang and Ce Bian and Chi Zhang and Ruiyang Sun and Yizhou Wang and Yaodong Yang},
journal = {arXiv preprint arXiv:2307.04657},
year = {2023}
}
``` |
wdcqc/starcraft-remastered-melee-maps | ---
tags:
- starcraft
- broodwar
- melee
- maps
license: unknown
language:
- en
- ko
pretty_name: Starcraft Remastered Melee Maps
size_categories: 1K<n<10K
task_categories:
- feature-extraction
- text-to-image
- image-to-image
- reinforcement-learning
task_ids:
- task-planning
splits:
- name: ashworld
num_bytes: 12,598,840
num_examples: 135
- name: badlands
num_bytes: 21,067,712
num_examples: 213
- name: desert
num_bytes: 19,505,010
num_examples: 185
- name: ice
num_bytes: 19,070,217
num_examples: 179
- name: install
num_bytes: 28,135
num_examples: 1
- name: jungle
num_bytes: 62,374,211
num_examples: 563
- name: platform
num_bytes: 23,324,208
num_examples: 265
- name: twilight
num_bytes: 28,311,253
num_examples: 274
---
## starcraft-remastered-melee-maps
This is a dataset containing 1,815 Starcraft:Remastered melee maps, categorized into tilesets.
The dataset is used to train this model: https://huggingface.co/wdcqc/starcraft-platform-terrain-32x32
The dataset is manually downloaded from Battle.net, bounding.net (scmscx.com) and broodwarmaps.com over a long period of time.
To use this dataset, extract the `staredit\\scenario.chk` files from the map files using StormLib, then refer to [Scenario.chk Format](http://www.staredit.net/wiki/index.php/Scenario.chk) to get data like text, terrain or resource placement from the map.
Alternatively download the dataset and put it in `<My Documents>\StarCraft\Maps`. You can play with your friends. |
liuyanchen1015/mnli_MULTI | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
splits:
- name: train
num_bytes: 79281363
num_examples: 384388
- name: dev_matched
num_bytes: 1983976
num_examples: 9779
- name: dev_mismatched
num_bytes: 2092314
num_examples: 9823
- name: test_matched
num_bytes: 1976499
num_examples: 9672
- name: test_mismatched
num_bytes: 2096238
num_examples: 9841
download_size: 58746057
dataset_size: 87430390
---
# Dataset Card for "mnli_MULTI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
awinml/pubmed_abstract_3_1k | ---
dataset_info:
features:
- name: pmid
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1524794
num_examples: 1000
download_size: 873865
dataset_size: 1524794
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Source:
https://huggingface.co/datasets/ywchoi/pubmed_abstract_3 |
Ubenwa/CryCeleb2023 | ---
viewer: false
dataset_info:
features:
- name: baby_id
dtype: string
- name: period
dtype: string
- name: duration
dtype: float64
- name: split
dtype: string
- name: chronological_index
dtype: string
- name: file_name
dtype: string
- name: file_id
dtype: string
splits:
- name: train
num_bytes: 522198700
num_examples: 18190
num_babies: 586
total_length (minutes): 268
- name: dev
num_bytes: 45498424
num_examples: 1614
num_babies: 40
total_length (minutes): 23
- name: test
num_bytes: 192743500
num_examples: 6289
num_babies: 160
total_length (minutes): 99
dataset_size: 760444720
num_examples: 26093
num_babies: 786
total_length (minutes): 391
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
size_categories:
- 10K<n<100K
extra_gated_fields:
Affilation (company or university): text
Country: text
I agree to use this data for non-commercial use ONLY (under Creative Commons Attribution-NonCommercial-NoDerivatives 4 International license): checkbox
---
# Dataset Card for "CryCeleb2023"
## Table of Contents
- [Dataset Card for "CryCeleb2023"](#dataset-card-for-cryceleb2023)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Splits](#data-splits)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [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://huggingface.co/datasets/Ubenwa/CryCeleb2023**
- **Repository: https://huggingface.co/datasets/Ubenwa/CryCeleb2023**
- **Paper: https://arxiv.org/abs/2305.00969**
- **Leaderboard: https://huggingface.co/spaces/competitions/CryCeleb2023**
- **Point of Contact: challenge@ubenwa.ai**
### Dataset Summary
The CryCeleb2023 dataset is a compilation of cries gathered from 786 infants from various hospitals. \
The 26k audio files make up 6.5 hours of pure expiration sounds. \
The dataset also contains information on the time of recording, which is either within the first hour(s) of life or \
upon hospital discharge, typically within 24 hours of birth.
### Supported Tasks and Leaderboards
[CryCeleb2023 competition](https://huggingface.co/spaces/competitions/CryCeleb2023)
## Dataset Structure
Audio folder contains short wav files (16 kHz wav PCM).
*audio* - folder with audio files structured by infant ID
```
audio/
train/
spk1/
B/
spk1_B_001.wav
...
spk6_B_001.wav
...
D/
spk1_D_001.wav
...
...
spk586
...
dev/
...(similar to train)...
test/
anonymous1/
B/
...
```
In this folder structure:
- spkN: folder with recordings corresponding to baby N
- B/D: time of recording (birth or discharge)
- 001, 002,, etc - chronological index of cry sound (expiration)
*metadata.csv* - metadata associated with each audio file
*dev_pairs.csv* - pairs of birth/discharge recordings used for evaluating development set (available to challenge participants)
*test_pairs.csv* - pairs of birth/discharge recordings used in CryCeleb2023 evaluation (public and private scores)
### Data Instances
Audio files 16 kHz wav PCM - manually segmented cry sounds (expirations)
### Data Splits
Number of Infants by Split and Time(s) of Recording(s)
| Time(s) of Recording | train | dev | test |
| --- | --- | --- | --- |
| Both birth and discharge | 348 | 40 | 160 |
| Only birth | 183 | 0 | 0 |
| Only discharge | 55 | 0 | 0 |
| | 586 | 40 | 160 |
### Source Data
Audio recordings of infant cries made by android application
### Annotations
#### Annotation process
- Manual segmentation of cry into three categories: expiration, inspiration, no cry
- Only expirations kept in this corpus
- Manual review to remove any PIIs
### Personal and Sensitive Information
PII such as intelligible background speech, etc, were removed from the data.
All identities are also anonymized.
## Considerations for Using the Data
### Discussion of Biases
The dataset only covers infants born in one country
### Other Known Limitations
Dataset only includes expirations.
Recording quality varies
## Additional Information
### Dataset Curators
Ubenwa.ai (contact: challenge@ubenwa.ai)
### Licensing Information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
[](https://creativecommons.org/licenses/cc-nc-nd/4.0/)
### Citation Information
Please cite the following paper if you use this dataset
```
@article{ubenwa2023cryceleb,
title={CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds},
author={David Budaghyan and Charles C. Onu and Arsenii Gorin and Cem Subakan and Doina Precup},
year={2023},
journal={preprint arXiv:2305.00969},
}
```
|
mehdidc/dataset-test | ---
dataset_info:
features:
- name: caption
dtype: string
- name: caption_source
dtype: string
- name: image_0_url
dtype: string
- name: image_1_url
dtype: string
- name: label_0
dtype: float64
- name: label_1
dtype: float64
- name: num_example_per_prompt
dtype: int64
- name: model_0
dtype: string
- name: model_1
dtype: string
- name: jpg_0
dtype: binary
- name: jpg_1
dtype: binary
- name: are_different
dtype: bool
- name: has_label
dtype: bool
splits:
- name: train
num_bytes: 292907
num_examples: 1
download_size: 300728
dataset_size: 292907
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cc-platform-links/platform-urls-sample-roberta-tiny-filtered | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: url
dtype: string
- name: label
dtype: int64
- name: true_label
dtype: int64
splits:
- name: train
num_bytes: 1738881
num_examples: 20739
download_size: 756212
dataset_size: 1738881
---
# Dataset Card for "platform-urls-sample-roberta-tiny-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sean0042/MMLU-medical | ---
dataset_info:
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
splits:
- name: dev
num_bytes: 15846.0
num_examples: 45
- name: test
num_bytes: 741698
num_examples: 1871
download_size: 396408
dataset_size: 757544.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
|
autoevaluate/autoeval-staging-eval-project-00ac2adb-9115200 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cifar10
eval_info:
task: image_multi_class_classification
model: jimypbr/cifar10_outputs
metrics: []
dataset_name: cifar10
dataset_config: plain_text
dataset_split: test
col_mapping:
image: img
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: jimypbr/cifar10_outputs
* Dataset: cifar10
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@davidberg](https://huggingface.co/davidberg) for evaluating this model. |
mstz/waveform_noise_v1 | ---
language:
- en
tags:
- waveformnoiseV1
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: WaveformNoiseV1
size_categories:
- 1K<n<5K
task_categories:
- tabular-classification
configs:
- waveformnoiseV1
- waveformnoiseV1_0
- waveformnoiseV1_1
- waveformnoiseV1_2
license: cc
---
# WaveformNoiseV1
The [WaveformNoiseV1 dataset](https://archive-beta.ics.uci.edu/dataset/107/waveform+database+generator+version+1) from the [UCI repository](https://archive-beta.ics.uci.edu/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-----------------------|---------------------------|-------------------------|
| waveformnoiseV1 | Multiclass classification.| |
| waveformnoiseV1_0 | Binary classification. | Is the image of class 0? |
| waveformnoiseV1_1 | Binary classification. | Is the image of class 1? |
| waveformnoiseV1_2 | Binary classification. | Is the image of class 2? | |
sam-mosaic/evesix-level0 | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 761935742
num_examples: 486455
download_size: 384732088
dataset_size: 761935742
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "evesix-level0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Snoopy04/hellaswag-de-harness-1k | ---
dataset_info:
features:
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: id
dtype: string
- name: ind
dtype: int64
- name: activity_label
dtype: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: label
dtype: string
splits:
- name: val
num_bytes: 1338606.4261315116
num_examples: 1000
download_size: 763101
dataset_size: 1338606.4261315116
configs:
- config_name: default
data_files:
- split: val
path: data/val-*
---
|
DaisyStar004/covid-llama2-500 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 317407
num_examples: 500
download_size: 181582
dataset_size: 317407
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "covid-llama2-500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TokenBender/sentence_retrieval_hindi_SFT | ---
license: apache-2.0
---
|
Seanxh/twitter_dataset_1713109230 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 34140
num_examples: 89
download_size: 19558
dataset_size: 34140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
shraddha18/training_dataset_without_decoded_Qlora_v2 | ---
license: apache-2.0
---
|
munozariasjm/tab_pib_4_7 | ---
license: mit
---
|
zicsx/subs | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 14021830159
num_examples: 39333242
download_size: 5716459284
dataset_size: 14021830159
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Kariander1/img_sketch | ---
license: cc0-1.0
dataset_info:
features:
- name: image
dtype: image
- name: sketch
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 660977948.0
num_examples: 8000
- name: validation
num_bytes: 82876916.0
num_examples: 1000
- name: test
num_bytes: 82804495.0
num_examples: 1000
download_size: 812243987
dataset_size: 826659359.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
lansinuote/cv.1.image_classification | ---
dataset_info:
features:
- name: labels
dtype:
class_label:
names:
'0': angular_leaf_spot
'1': bean_rust
'2': healthy
- name: pixel_values
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 625388016
num_examples: 1034
- name: validation
num_bytes: 80441592
num_examples: 133
- name: test
num_bytes: 77417472
num_examples: 128
download_size: 0
dataset_size: 783247080
---
# Dataset Card for "cv.1.image_classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
betogaunt2/novosom | ---
license: openrail
---
|
fblgit/tree-of-knowledge | ---
license: lgpl-3.0
---
# tree-of-knowledge-llm
ToK aka Tree of Knowledge for Large Language Models LLM. It's a novel dataset that inspires knowledge symbolic correlation in simple input and output prompts.
https://github.com/fblgit/tree-of-knowledge-llm
The set experimentially can be used with multiple purposes:
* Knowledge Extraction from a Model
* Fine Tuning a model with newer data
* Create Granular Domain Knowledge Sets
* Improve training performance
Syntax Example:
```
{
"instruction": "Describe energy",
"input": "",
"output": "Energy AS ability TO do work OR cause change WITHIN system && forms of energy==[kinetic, potential, thermal, chemical, ... [TYPES]] && conservation of energy==law STATING energy CANNOT be created OR destroyed ONLY transformed BETWEEN forms && energy sources==[fossil fuels, nuclear, solar, wind, ... [
EXAMPLES]] USED TO power human activities"
}
```
## Characteristics
* Introduces condensation masking with `...`
* Introduces hints keywords. example: `[ADVANTAGES]`, `[CHARACTERISTICS]`, `[RULES]`.
* Introduces directional keywords. example: `AS`, `AND`, `IN`, `BETWEEN`, `RANGING`.
* Introduces approach keywords. example: `NOTATED`, `PREDICTING`, `CALCULATED`
* Introduces efficient aggrupations keyword `===`
* Introduces separated relationship keyword `&&`
## Changelog
- 2023-05-20 - Released the first version of the dataset, illustrative examples.
- 2023-05-21 - Added the first 3000 dataset items under `data/` folder. They will be marked with the date of the dataset version.
## Citations
Please cite this repository if you the code.
```
@misc{tree-of-knowledge,
author = {Xavier M},
title = {Tree of Knowledge: ToK aka Tree of Knowledge dataset for Large Language Models LLM,
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/fblgit/tree-of-knowledge}},
}
``` |
mertllc/fourties-female | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 144931157.368
num_examples: 6802
download_size: 137277833
dataset_size: 144931157.368
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
caldervf/cicero_clean_dataset | ---
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: content
dtype: string
- name: clean_content
dtype: string
splits:
- name: train
num_bytes: 13758326
num_examples: 1143
download_size: 0
dataset_size: 13758326
---
# Dataset Card for "cicero_clean_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
manirai91/ebiquity-v2-stemmed | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: ebiquity-v2-stemmed
splits:
- name: train
num_bytes: 2192488
num_examples: 3289
download_size: 1414009
dataset_size: 2192488
---
|
LazarusNLP/mini_pile_cc | ---
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: pile_set_name
dtype: string
splits:
- name: train
num_bytes: 56119925050.245285
num_examples: 10000000
download_size: 26514273271
dataset_size: 56119925050.245285
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference | ---
pretty_name: Evaluation run of dvruette/oasst-pythia-12b-reference
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dvruette/oasst-pythia-12b-reference](https://huggingface.co/dvruette/oasst-pythia-12b-reference)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T19:14:07.226959](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference/blob/main/results_2023-10-21T19-14-07.226959.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001363255033557047,\n\
\ \"em_stderr\": 0.00037786091964608703,\n \"f1\": 0.05910759228187943,\n\
\ \"f1_stderr\": 0.0013983745600314773,\n \"acc\": 0.3308481527645552,\n\
\ \"acc_stderr\": 0.008212170959780564\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.00037786091964608703,\n\
\ \"f1\": 0.05910759228187943,\n \"f1_stderr\": 0.0013983745600314773\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.012130401819560273,\n \
\ \"acc_stderr\": 0.0030152942428909465\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6495659037095501,\n \"acc_stderr\": 0.013409047676670182\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dvruette/oasst-pythia-12b-reference
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_21T19_14_07.226959
path:
- '**/details_harness|drop|3_2023-10-21T19-14-07.226959.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T19-14-07.226959.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T19_14_07.226959
path:
- '**/details_harness|gsm8k|5_2023-10-21T19-14-07.226959.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T19-14-07.226959.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T19_14_07.226959
path:
- '**/details_harness|winogrande|5_2023-10-21T19-14-07.226959.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T19-14-07.226959.parquet'
- config_name: results
data_files:
- split: 2023_10_21T19_14_07.226959
path:
- results_2023-10-21T19-14-07.226959.parquet
- split: latest
path:
- results_2023-10-21T19-14-07.226959.parquet
---
# Dataset Card for Evaluation run of dvruette/oasst-pythia-12b-reference
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dvruette/oasst-pythia-12b-reference
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [dvruette/oasst-pythia-12b-reference](https://huggingface.co/dvruette/oasst-pythia-12b-reference) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T19:14:07.226959](https://huggingface.co/datasets/open-llm-leaderboard/details_dvruette__oasst-pythia-12b-reference/blob/main/results_2023-10-21T19-14-07.226959.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964608703,
"f1": 0.05910759228187943,
"f1_stderr": 0.0013983745600314773,
"acc": 0.3308481527645552,
"acc_stderr": 0.008212170959780564
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964608703,
"f1": 0.05910759228187943,
"f1_stderr": 0.0013983745600314773
},
"harness|gsm8k|5": {
"acc": 0.012130401819560273,
"acc_stderr": 0.0030152942428909465
},
"harness|winogrande|5": {
"acc": 0.6495659037095501,
"acc_stderr": 0.013409047676670182
}
}
```
### 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] |
nlplabtdtu/closed-QA-vi | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
- name: hint
dtype: string
- name: ok
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 11217015
num_examples: 6380
download_size: 5360083
dataset_size: 11217015
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "closed-QA-vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JamesSpray/txsa_twitter_sentiment_analysis | ---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1049869
num_examples: 8539
- name: validation
num_bytes: 145889
num_examples: 1000
download_size: 834300
dataset_size: 1195758
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
wshi83/EHRAgent-eicu | ---
license: apache-2.0
---
|
bibidentuhanoi/BMO_BASE_TEXT | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 154049
num_examples: 278
download_size: 84465
dataset_size: 154049
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "BMO_BASE_TEXT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joefox/Mozilla_Common_Voice_ru_test_noise | ---
license: apache-2.0
---
### Dataset Summary
Augmented part of the test data of the Mozilla Common Voice (part 10, ru, test) dataset.
As a basis, the original part of the test was taken, and augmentation was carried out to add extraneous noise.
Part dataset: test
|
iamnguyen/ds_by_sys_prompt_11 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 80187902.7381001
num_examples: 47015
download_size: 17360728
dataset_size: 80187902.7381001
---
# Dataset Card for "ds_by_sys_prompt_11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nicolas-BZRD/uld_loss_Mistral-7B-Instruct-v0.2-dialogsum | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: context
dtype: string
- name: summary
dtype: string
- name: summary_generated
dtype: string
splits:
- name: train
num_bytes: 13322445
num_examples: 12460
- name: validation
num_bytes: 522817
num_examples: 500
download_size: 7913164
dataset_size: 13845262
---
# Dataset Card for "uld_loss_Mistral-7B-Instruct-v0.2-dialogsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jessicay12138/RetireSent | ---
license: cc-by-4.0
language:
- en
tags:
- finance
size_categories:
- 1K<n<10K
---
1035 labeled sentences from English news sources about retirement funds |
CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yuuki_setsuna/優木せつ菜/유키세츠나 (Love Live! School Idol Festival ALL STARS)
This is the dataset of yuuki_setsuna/優木せつ菜/유키세츠나 (Love Live! School Idol Festival ALL STARS), containing 500 images and their tags.
The core tags of this character are `long_hair, black_hair, bangs, grey_eyes, breasts, one_side_up, sidelocks, hair_ornament, black_eyes, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 898.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 416.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1323 | 960.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 746.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1323 | 1.51 GiB | [Download](https://huggingface.co/datasets/CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/yuuki_setsuna_loveliveschoolidolfestivalallstars',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, nijigasaki_academy_school_uniform, smile, solo, blush, short_sleeves, summer_uniform, upper_body, simple_background, white_background, white_shirt, black_vest, collared_shirt, ribbon, skirt |
| 1 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, nijigasaki_academy_school_uniform, plaid_skirt, pleated_skirt, short_sleeves, simple_background, solo, white_background, white_shirt, collared_shirt, neck_ribbon, smile, summer_uniform, blue_vest, dress_shirt, open_mouth, black_vest, pink_ribbon |
| 2 | 6 |  |  |  |  |  | 1girl, blush, cleavage, collarbone, looking_at_viewer, solo, upper_body, simple_background, smile, white_background, bra, off_shoulder |
| 3 | 5 |  |  |  |  |  | 1girl, blush, cleavage, looking_at_viewer, paw_gloves, solo, open_mouth, simple_background, smile, upper_body, bear_ears, dress, fake_animal_ears, large_breasts, red_bowtie, short_sleeves, red_background, white_background |
| 4 | 5 |  |  |  |  |  | 1girl, feather_hair_ornament, hair_flower, looking_at_viewer, solo, white_gloves, blush, smile, thighhighs, asymmetrical_legwear, happy_birthday, upper_body |
| 5 | 5 |  |  |  |  |  | 1girl, feather_hair_ornament, hair_flower, looking_at_viewer, red_bowtie, solo, white_gloves, white_shirt, blush, red_skirt, smile, collared_shirt, frilled_skirt, center_frills, simple_background, sitting, white_background, yellow_jacket |
| 6 | 8 |  |  |  |  |  | blush, cropped_jacket, feather_hair_ornament, hair_flower, looking_at_viewer, red_bowtie, red_skirt, white_shirt, 1girl, :d, frilled_skirt, open_mouth, solo, white_gloves, center_frills, frilled_shirt, mismatched_legwear, yellow_jacket, blue_rose, blue_thighhighs, idol_clothes, outstretched_arm, upper_teeth_only, double-breasted, half_gloves, short_sleeves, yellow_rose, black_footwear, full_body, knee_boots, simple_background, white_background |
| 7 | 7 |  |  |  |  |  | 1girl, earrings, hat, looking_at_viewer, necktie, fingerless_gloves, red_gloves, solo, fire, blush, smile |
| 8 | 25 |  |  |  |  |  | 1girl, fingerless_gloves, looking_at_viewer, red_gloves, red_headwear, solo, smile, collared_shirt, mini_hat, white_shirt, short_sleeves, blush, open_mouth, skirt, earrings, red_vest, flower, purple_necktie, frilled_shirt |
| 9 | 14 |  |  |  |  |  | 1girl, cleavage, collarbone, braid, double_bun, looking_at_viewer, red_bikini, solo, blush, navel, suspender_shorts, white_background, simple_background, striped_bikini |
| 10 | 9 |  |  |  |  |  | 1girl, bikini, double_bun, looking_at_viewer, solo, braid, cleavage, collarbone, hair_flower, navel, tattoo, earrings, cloud, smile, suspender_shorts, blue_sky, blush, heart |
| 11 | 6 |  |  |  |  |  | 1girl, midriff, navel, red_sleeves, single_glove, single_sleeve, solo, belt, black_shorts, collarbone, fire, star_earrings, asymmetrical_sleeves, epaulettes, jacket, looking_at_viewer, open_mouth, see-through, asymmetrical_gloves |
| 12 | 7 |  |  |  |  |  | 1girl, solo, black_pantyhose, blush, hairclip, red_hoodie, legwear_under_shorts, looking_at_viewer, smile, collarbone, long_sleeves, open_mouth, shoulder_bag, handbag |
| 13 | 7 |  |  |  |  |  | 1girl, cheerleader, midriff, navel, pom_pom_(cheerleading), solo, hair_flower, headphones, looking_at_viewer, red_skirt, smile, blush, crop_top, headset, miniskirt, sleeveless_shirt, arm_up, happy_birthday, holding, pleated_skirt, socks |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | nijigasaki_academy_school_uniform | smile | solo | blush | short_sleeves | summer_uniform | upper_body | simple_background | white_background | white_shirt | black_vest | collared_shirt | ribbon | skirt | plaid_skirt | pleated_skirt | neck_ribbon | blue_vest | dress_shirt | open_mouth | pink_ribbon | cleavage | collarbone | bra | off_shoulder | paw_gloves | bear_ears | dress | fake_animal_ears | large_breasts | red_bowtie | red_background | feather_hair_ornament | hair_flower | white_gloves | thighhighs | asymmetrical_legwear | happy_birthday | red_skirt | frilled_skirt | center_frills | sitting | yellow_jacket | cropped_jacket | :d | frilled_shirt | mismatched_legwear | blue_rose | blue_thighhighs | idol_clothes | outstretched_arm | upper_teeth_only | double-breasted | half_gloves | yellow_rose | black_footwear | full_body | knee_boots | earrings | hat | necktie | fingerless_gloves | red_gloves | fire | red_headwear | mini_hat | red_vest | flower | purple_necktie | braid | double_bun | red_bikini | navel | suspender_shorts | striped_bikini | bikini | tattoo | cloud | blue_sky | heart | midriff | red_sleeves | single_glove | single_sleeve | belt | black_shorts | star_earrings | asymmetrical_sleeves | epaulettes | jacket | see-through | asymmetrical_gloves | black_pantyhose | hairclip | red_hoodie | legwear_under_shorts | long_sleeves | shoulder_bag | handbag | cheerleader | pom_pom_(cheerleading) | headphones | crop_top | headset | miniskirt | sleeveless_shirt | arm_up | holding | socks |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:------------------------------------|:--------|:-------|:--------|:----------------|:-----------------|:-------------|:--------------------|:-------------------|:--------------|:-------------|:-----------------|:---------|:--------|:--------------|:----------------|:--------------|:------------|:--------------|:-------------|:--------------|:-----------|:-------------|:------|:---------------|:-------------|:------------|:--------|:-------------------|:----------------|:-------------|:-----------------|:------------------------|:--------------|:---------------|:-------------|:-----------------------|:-----------------|:------------|:----------------|:----------------|:----------|:----------------|:-----------------|:-----|:----------------|:---------------------|:------------|:------------------|:---------------|:-------------------|:-------------------|:------------------|:--------------|:--------------|:-----------------|:------------|:-------------|:-----------|:------|:----------|:--------------------|:-------------|:-------|:---------------|:-----------|:-----------|:---------|:-----------------|:--------|:-------------|:-------------|:--------|:-------------------|:-----------------|:---------|:---------|:--------|:-----------|:--------|:----------|:--------------|:---------------|:----------------|:-------|:---------------|:----------------|:-----------------------|:-------------|:---------|:--------------|:----------------------|:------------------|:-----------|:-------------|:-----------------------|:---------------|:---------------|:----------|:--------------|:-------------------------|:-------------|:-----------|:----------|:------------|:-------------------|:---------|:----------|:--------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | | X | X | X | X | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | | X | X | X | | | X | X | X | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | | X | X | X | X | | X | X | X | | | | | | | | | | | X | | X | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | | X | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | X | X | X | | | | X | X | X | | X | | | | | | | | | | | | | | | | | | | X | | X | X | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | X | | | X | X | X | | | X | X | X | | | | | | | | | | X | | | | | | | | | | | X | | X | X | X | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 25 |  |  |  |  |  | X | X | | X | X | X | X | | | | | X | | X | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 14 |  |  |  |  |  | X | X | | | X | X | | | | X | X | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 9 |  |  |  |  |  | X | X | | X | X | X | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 6 |  |  |  |  |  | X | X | | | X | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 12 | 7 |  |  |  |  |  | X | X | | X | X | X | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | |
| 13 | 7 |  |  |  |  |  | X | X | | X | X | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
Sugisaku8/SCRDataSet | ---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
- text2text-generation
language:
- ja
- en
pretty_name: SCR Data Set
size_categories:
- n<1K
---
# SCR Data Set
## Dataset Details
This dataset is for tuning already existing models for use in school settings.
## Dataset Details.
### Dataset Description
<! -- A longer summary of what this dataset is. -->.
### dataset source [optional] ** [more info needed] ** [more info needed] ** [more info needed
Based on data from Wikipedia or other sources,
constructed independently.
## Usage
Tuning of already published models
### Direct use
Tuning for flexible use of AI in school settings
Such as.
### Out of range use
Malicious use is strictly prohibited.
Third parties reserve the right to determine the criteria for malicious intent.
## Structure of the dataset
It is made in JSON and has this structure.
## Creation of dataset.
### Reason for curation
To publish AI models tuned for school sites.
### Copyright
Copyright 2024 Sugisaku8
All rights reserved |
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xl_mode_C_HM_T_A_OCR_rices_ns_500 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
sequence: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text
num_bytes: 600963
num_examples: 500
- name: fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full__text
num_bytes: 583968
num_examples: 500
- name: fewshot_0
num_bytes: 585423
num_examples: 500
download_size: 331241
dataset_size: 1770354
---
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xl_mode_C_HM_T_A_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bigbio/tmvar_v2 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: tmVar v2
homepage: https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for tmVar v2
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
This dataset contains 158 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them.
It can be used for NER tasks and NED tasks, This dataset has a single split
## Citation Information
```
@article{wei2018tmvar,
title={tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine},
author={Wei, Chih-Hsuan and Phan, Lon and Feltz, Juliana and Maiti, Rama and Hefferon, Tim and Lu, Zhiyong},
journal={Bioinformatics},
volume={34},
number={1},
pages={80--87},
year={2018},
publisher={Oxford University Press}
}
```
|
liuyanchen1015/MULTI_VALUE_mrpc_my_i | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 3068
num_examples: 13
- name: train
num_bytes: 5971
num_examples: 22
- name: validation
num_bytes: 683
num_examples: 3
download_size: 17511
dataset_size: 9722
---
# Dataset Card for "MULTI_VALUE_mrpc_my_i"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kyujinpy/OpenOrca-ko-v2 | ---
license: cc-by-nc-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 41592589
num_examples: 19468
download_size: 21611641
dataset_size: 41592589
---
## OpenOrca-Ko-v2
1. NIV // 약 1500개
2. FLAN // 약 9000개
3. T0 // 약 6000개
4. CoT // 약 2000개
> Dataset 구성
- 수작업으로 고친 내용(v2)
1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...)
2. KoCoT 데이터셋 제거.
3. Yes, True, False 등등 일부 답변 수정
> Post-processing 작업 내용
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` |
anan-2024/twitter_dataset_1713145158 | ---
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|
bizoffermark/nerdy-ghibli | ---
dataset_info:
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---
# Dataset Card for "nerdy-ghibli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sahityas/goodreads-llama-7b-a | ---
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---
|
pcuenq/oxford-pets | ---
tags:
- pets
- oxford
license: cc-by-sa-4.0
license_details: https://www.robots.ox.ac.uk/~vgg/data/pets/
pretty_name: Oxford-IIIT Pet Dataset (no annotations)
source_datasets: https://www.robots.ox.ac.uk/~vgg/data/pets/
task_categories:
- image-classification
---
# Oxford-IIIT Pet Dataset
Images from [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/). Only images and labels have been pushed, segmentation annotations were ignored.
- **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/pets/
License:
Same as the original dataset.
|
wessmetal/edufalaschi | ---
license: bsd
---
|
universalner/universal_ner | ---
license: cc-by-sa-4.0
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---
# Dataset Card for Universal NER
Upcoming!
arXiv: https://huggingface.co/papers/2311.09122 |
ITESM/dataset | ---
license: mit
---
|
bcui19/UC-first-turn-no-sys | ---
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dataset_size: 523712309
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "UC-first-turn-no-sys"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jan-hq/finance_mixed_50_binarized | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 190587540.62714094
num_examples: 125117
- name: test
num_bytes: 162744958
num_examples: 107048
download_size: 158985767
dataset_size: 353332498.62714094
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
This is a mixed dataset between Finance domain QA and General QA with the ratio 1:1.
- [Finance dataset](https://huggingface.co/datasets/jan-hq/finance_alpaca_binarized)
- [General dataset](https://huggingface.co/datasets/jan-hq/openhermes-2.5_binarized) |
5CD-AI/Vietnamese-yfcc15m-OpenAICLIP | ---
task_categories:
- image-to-text
- text-to-image
- visual-question-answering
language:
- en
- vi
size_categories:
- 10M<n<100M
--- |
Isotonic/massive_nli_dataset | ---
license: apache-2.0
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 150300464
num_examples: 1018574
- name: test
num_bytes: 32168924
num_examples: 218266
- name: valid
num_bytes: 32238483
num_examples: 218266
download_size: 137255997
dataset_size: 214707871
task_categories:
- zero-shot-classification
language:
- en
size_categories:
- 1M<n<10M
--- |
lmiro/testing | ---
license: afl-3.0
---
|
arpitdvd/sample_font_aesthetics_ds | ---
license: mit
---
|
open-llm-leaderboard/details_postbot__distilgpt2-emailgen | ---
pretty_name: Evaluation run of postbot/distilgpt2-emailgen
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [postbot/distilgpt2-emailgen](https://huggingface.co/postbot/distilgpt2-emailgen)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_postbot__distilgpt2-emailgen_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-13T13:25:05.974225](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__distilgpt2-emailgen_public/blob/main/results_2023-11-13T13-25-05.974225.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\": 0.2585985031430374,\n\
\ \"acc_stderr\": 0.03091312867789808,\n \"acc_norm\": 0.2592605342225761,\n\
\ \"acc_norm_stderr\": 0.03173517189546408,\n \"mc1\": 0.24357405140758873,\n\
\ \"mc1_stderr\": 0.015026354824910782,\n \"mc2\": 0.46170278335459186,\n\
\ \"mc2_stderr\": 0.01541047587026832,\n \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.011639052013422831,\n \"f1_stderr\"\
: 0.0006056902097790024\n },\n \"harness|arc:challenge|25\": {\n \"\
acc\": 0.18600682593856654,\n \"acc_stderr\": 0.01137094018326675,\n \
\ \"acc_norm\": 0.2175767918088737,\n \"acc_norm_stderr\": 0.012057262020972497\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2687711611232822,\n\
\ \"acc_stderr\": 0.004424146562746121,\n \"acc_norm\": 0.27524397530372435,\n\
\ \"acc_norm_stderr\": 0.004457243336616497\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.32592592592592595,\n\
\ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.32592592592592595,\n\
\ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17105263157894737,\n \"acc_stderr\": 0.030643607071677088,\n\
\ \"acc_norm\": 0.17105263157894737,\n \"acc_norm_stderr\": 0.030643607071677088\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.02544786382510861,\n\
\ \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.02544786382510861\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n\
\ \"acc_stderr\": 0.034370793441061344,\n \"acc_norm\": 0.2152777777777778,\n\
\ \"acc_norm_stderr\": 0.034370793441061344\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\
: 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\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\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n\
\ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.26,\n \"acc_stderr\": 0.044084400227680814,\n \"acc_norm\": 0.26,\n\
\ \"acc_norm_stderr\": 0.044084400227680814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.225531914893617,\n \"acc_stderr\": 0.027321078417387533,\n\
\ \"acc_norm\": 0.225531914893617,\n \"acc_norm_stderr\": 0.027321078417387533\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\
\ \"acc_stderr\": 0.04096985139843671,\n \"acc_norm\": 0.2543859649122807,\n\
\ \"acc_norm_stderr\": 0.04096985139843671\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438014,\n\
\ \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438014\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\": 0.15079365079365079,\n\
\ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\
\ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n\
\ \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n\
\ \"acc_norm_stderr\": 0.02645087448904277\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.30049261083743845,\n \"acc_stderr\": 0.03225799476233484,\n\
\ \"acc_norm\": 0.30049261083743845,\n \"acc_norm_stderr\": 0.03225799476233484\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\"\
: 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.25252525252525254,\n \"acc_stderr\": 0.030954055470365904,\n \"\
acc_norm\": 0.25252525252525254,\n \"acc_norm_stderr\": 0.030954055470365904\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.23834196891191708,\n \"acc_stderr\": 0.030748905363909902,\n\
\ \"acc_norm\": 0.23834196891191708,\n \"acc_norm_stderr\": 0.030748905363909902\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.22564102564102564,\n \"acc_stderr\": 0.021193632525148543,\n\
\ \"acc_norm\": 0.22564102564102564,\n \"acc_norm_stderr\": 0.021193632525148543\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\": 0.35294117647058826,\n \"acc_stderr\": 0.031041941304059288,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.031041941304059288\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.21651376146788992,\n \"acc_stderr\": 0.017658710594443128,\n \"\
acc_norm\": 0.21651376146788992,\n \"acc_norm_stderr\": 0.017658710594443128\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\": 0.25980392156862747,\n\
\ \"acc_stderr\": 0.03077855467869326,\n \"acc_norm\": 0.25980392156862747,\n\
\ \"acc_norm_stderr\": 0.03077855467869326\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035307,\n\
\ \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035307\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2600896860986547,\n\
\ \"acc_stderr\": 0.02944249558585746,\n \"acc_norm\": 0.2600896860986547,\n\
\ \"acc_norm_stderr\": 0.02944249558585746\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.256198347107438,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\
: 0.256198347107438,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n \
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.03462419931615624,\n\
\ \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.03462419931615624\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\
\ \"acc_stderr\": 0.042032772914677614,\n \"acc_norm\": 0.26785714285714285,\n\
\ \"acc_norm_stderr\": 0.042032772914677614\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.18803418803418803,\n\
\ \"acc_stderr\": 0.02559819368665226,\n \"acc_norm\": 0.18803418803418803,\n\
\ \"acc_norm_stderr\": 0.02559819368665226\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.2554278416347382,\n\
\ \"acc_stderr\": 0.015594955384455766,\n \"acc_norm\": 0.2554278416347382,\n\
\ \"acc_norm_stderr\": 0.015594955384455766\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n\
\ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\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\": 0.2549019607843137,\n \"acc_stderr\": 0.02495418432487991,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.02495418432487991\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.28938906752411575,\n\
\ \"acc_stderr\": 0.025755865922632924,\n \"acc_norm\": 0.28938906752411575,\n\
\ \"acc_norm_stderr\": 0.025755865922632924\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.025171041915309684,\n\
\ \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.025171041915309684\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.19858156028368795,\n \"acc_stderr\": 0.023798301637942106,\n \
\ \"acc_norm\": 0.19858156028368795,\n \"acc_norm_stderr\": 0.023798301637942106\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24837027379400262,\n\
\ \"acc_stderr\": 0.011035212598034501,\n \"acc_norm\": 0.24837027379400262,\n\
\ \"acc_norm_stderr\": 0.011035212598034501\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\
\ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.24836601307189543,\n \"acc_stderr\": 0.017479487001364764,\n \
\ \"acc_norm\": 0.24836601307189543,\n \"acc_norm_stderr\": 0.017479487001364764\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.24545454545454545,\n\
\ \"acc_stderr\": 0.041220665028782834,\n \"acc_norm\": 0.24545454545454545,\n\
\ \"acc_norm_stderr\": 0.041220665028782834\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2653061224489796,\n \"acc_stderr\": 0.028263889943784596,\n\
\ \"acc_norm\": 0.2653061224489796,\n \"acc_norm_stderr\": 0.028263889943784596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n\
\ \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.2537313432835821,\n\
\ \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.19879518072289157,\n\
\ \"acc_stderr\": 0.031069390260789424,\n \"acc_norm\": 0.19879518072289157,\n\
\ \"acc_norm_stderr\": 0.031069390260789424\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\": 0.24357405140758873,\n\
\ \"mc1_stderr\": 0.015026354824910782,\n \"mc2\": 0.46170278335459186,\n\
\ \"mc2_stderr\": 0.01541047587026832\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.516179952644041,\n \"acc_stderr\": 0.014045126130978603\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\"\
: 0.0,\n \"f1\": 0.011639052013422831,\n \"f1_stderr\": 0.0006056902097790024\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/postbot/distilgpt2-emailgen
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|arc:challenge|25_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|drop|3_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|gsm8k|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hellaswag|10_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-13T13-25-05.974225.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-13T13-25-05.974225.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- '**/details_harness|winogrande|5_2023-11-13T13-25-05.974225.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-13T13-25-05.974225.parquet'
- config_name: results
data_files:
- split: 2023_11_13T13_25_05.974225
path:
- results_2023-11-13T13-25-05.974225.parquet
- split: latest
path:
- results_2023-11-13T13-25-05.974225.parquet
---
# Dataset Card for Evaluation run of postbot/distilgpt2-emailgen
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/postbot/distilgpt2-emailgen
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [postbot/distilgpt2-emailgen](https://huggingface.co/postbot/distilgpt2-emailgen) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_postbot__distilgpt2-emailgen_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-13T13:25:05.974225](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__distilgpt2-emailgen_public/blob/main/results_2023-11-13T13-25-05.974225.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": 0.2585985031430374,
"acc_stderr": 0.03091312867789808,
"acc_norm": 0.2592605342225761,
"acc_norm_stderr": 0.03173517189546408,
"mc1": 0.24357405140758873,
"mc1_stderr": 0.015026354824910782,
"mc2": 0.46170278335459186,
"mc2_stderr": 0.01541047587026832,
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.011639052013422831,
"f1_stderr": 0.0006056902097790024
},
"harness|arc:challenge|25": {
"acc": 0.18600682593856654,
"acc_stderr": 0.01137094018326675,
"acc_norm": 0.2175767918088737,
"acc_norm_stderr": 0.012057262020972497
},
"harness|hellaswag|10": {
"acc": 0.2687711611232822,
"acc_stderr": 0.004424146562746121,
"acc_norm": 0.27524397530372435,
"acc_norm_stderr": 0.004457243336616497
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.040491220417025055,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.040491220417025055
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17105263157894737,
"acc_stderr": 0.030643607071677088,
"acc_norm": 0.17105263157894737,
"acc_norm_stderr": 0.030643607071677088
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2188679245283019,
"acc_stderr": 0.02544786382510861,
"acc_norm": 0.2188679245283019,
"acc_norm_stderr": 0.02544786382510861
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2152777777777778,
"acc_stderr": 0.034370793441061344,
"acc_norm": 0.2152777777777778,
"acc_norm_stderr": 0.034370793441061344
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.23,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.23,
"acc_norm_stderr": 0.042295258468165065
},
"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": 0.24509803921568626,
"acc_stderr": 0.04280105837364395,
"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.04280105837364395
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.26,
"acc_stderr": 0.044084400227680814,
"acc_norm": 0.26,
"acc_norm_stderr": 0.044084400227680814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.225531914893617,
"acc_stderr": 0.027321078417387533,
"acc_norm": 0.225531914893617,
"acc_norm_stderr": 0.027321078417387533
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2543859649122807,
"acc_stderr": 0.04096985139843671,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.04096985139843671
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2896551724137931,
"acc_stderr": 0.03780019230438014,
"acc_norm": 0.2896551724137931,
"acc_norm_stderr": 0.03780019230438014
},
"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": 0.15079365079365079,
"acc_stderr": 0.03200686497287392,
"acc_norm": 0.15079365079365079,
"acc_norm_stderr": 0.03200686497287392
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.3161290322580645,
"acc_stderr": 0.02645087448904277,
"acc_norm": 0.3161290322580645,
"acc_norm_stderr": 0.02645087448904277
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.30049261083743845,
"acc_stderr": 0.03225799476233484,
"acc_norm": 0.30049261083743845,
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}
```
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