id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
semeru/code-code-galeras-code-completion-from-docstring-3k-deduped | 2023-10-01T17:09:46.000Z | [
"region:us"
] | semeru | null | null | null | 0 | 0 | Entry not found |
semeru/text-code-galeras-code-generation-from-docstring-3k-deduped | 2023-10-01T17:11:08.000Z | [
"region:us"
] | semeru | null | null | null | 0 | 0 | Entry not found |
semeru/code-text-galeras-commit-generation-3k-deduped | 2023-10-01T17:14:31.000Z | [
"region:us"
] | semeru | null | null | null | 0 | 0 | Entry not found |
semeru/code-text-galeras-code-summarization-3k-deduped | 2023-10-01T17:16:14.000Z | [
"region:us"
] | semeru | null | null | null | 1 | 0 | Entry not found |
justinlamlamlam/wiki_titles_with_embeddings | 2023-10-01T17:27:52.000Z | [
"region:us"
] | justinlamlamlam | null | null | null | 0 | 0 | Entry not found |
Binho7/rodrigodelara | 2023-10-01T17:35:34.000Z | [
"license:openrail",
"region:us"
] | Binho7 | null | null | null | 0 | 0 | ---
license: openrail
---
|
Zerenidel/silhouette | 2023-10-01T18:08:44.000Z | [
"region:us"
] | Zerenidel | null | null | null | 0 | 0 | Entry not found |
taesiri/TinyStories-Farsi | 2023-10-10T13:29:20.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:fa",
"language:en",
"license:cdla-sharing-1.0",
"Persian",
"Farsi",
"English2Farsi",
"Farsi2English",
"arxiv:2305.07759",
"region:us"
] | taesiri | null | null | null | 0 | 0 | ---
license: cdla-sharing-1.0
task_categories:
- text-generation
- text2text-generation
language:
- fa
- en
tags:
- Persian
- Farsi
- English2Farsi
- Farsi2English
pretty_name: Tiny Stories - Farsi
size_categories:
- 10K<n<100K
---
# Tiny Stories Farsi
The _Tiny Stories Farsi_ project is a continuous effort to translate the [Tiny Stories dataset](https://huggingface.co/datasets/roneneldan/TinyStories) into the Persian (Farsi) language. The primary goal is to produce a high-quality Farsi dataset, maintaining equivalency with the original English version, and subsequently to utilize it for training language models in Farsi. This seeks to affirm that the advancements and trends observed in English language models are replicable and applicable in other languages. Thus far, the project has translated over 27,000 entries from the validation set, originally created by `GPT-4`, into Farsi, using the `Claude-2.0` language model for the translation process. The project remains active and welcomes ongoing contributions and collaborative efforts towards the enrichment of non-English language data in the realm of machine learning and artificial intelligence.
Original paper: [TinyStories: How Small Can Language Models Be and Still Speak Coherent English?](https://arxiv.org/abs/2305.07759.) |
Taha8210/bell-test | 2023-10-01T18:58:58.000Z | [
"region:us"
] | Taha8210 | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.2.1 | 2023-10-01T18:59:30.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of jondurbin/airoboros-c34b-2.2.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-c34b-2.2.1](https://huggingface.co/jondurbin/airoboros-c34b-2.2.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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_jondurbin__airoboros-c34b-2.2.1\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-10-01T18:58:09.868261](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.2.1/blob/main/results_2023-10-01T18-58-09.868261.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\": 0.5540698152139825,\n\
\ \"acc_stderr\": 0.03493200966396464,\n \"acc_norm\": 0.5578156434457693,\n\
\ \"acc_norm_stderr\": 0.03491885315361723,\n \"mc1\": 0.3463892288861689,\n\
\ \"mc1_stderr\": 0.01665699710912514,\n \"mc2\": 0.5136120601402686,\n\
\ \"mc2_stderr\": 0.015251718211134593\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5179180887372014,\n \"acc_stderr\": 0.014602005585490978,\n\
\ \"acc_norm\": 0.5469283276450512,\n \"acc_norm_stderr\": 0.014546892052005626\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5763792073292173,\n\
\ \"acc_stderr\": 0.004931219148182242,\n \"acc_norm\": 0.7683728340967935,\n\
\ \"acc_norm_stderr\": 0.004210098571170223\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\
\ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\
\ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5723684210526315,\n \"acc_stderr\": 0.04026097083296562,\n\
\ \"acc_norm\": 0.5723684210526315,\n \"acc_norm_stderr\": 0.04026097083296562\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5358490566037736,\n \"acc_stderr\": 0.030693675018458003,\n\
\ \"acc_norm\": 0.5358490566037736,\n \"acc_norm_stderr\": 0.030693675018458003\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5694444444444444,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.5694444444444444,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.4624277456647399,\n\
\ \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.4624277456647399,\n\
\ \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383889,\n\
\ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383889\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4851063829787234,\n \"acc_stderr\": 0.032671518489247764,\n\
\ \"acc_norm\": 0.4851063829787234,\n \"acc_norm_stderr\": 0.032671518489247764\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\
acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.632258064516129,\n\
\ \"acc_stderr\": 0.02743086657997347,\n \"acc_norm\": 0.632258064516129,\n\
\ \"acc_norm_stderr\": 0.02743086657997347\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4187192118226601,\n \"acc_stderr\": 0.03471192860518468,\n\
\ \"acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.03471192860518468\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\"\
: 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.036085410115739666,\n\
\ \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.036085410115739666\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7121212121212122,\n \"acc_stderr\": 0.03225883512300992,\n \"\
acc_norm\": 0.7121212121212122,\n \"acc_norm_stderr\": 0.03225883512300992\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7564766839378239,\n \"acc_stderr\": 0.030975436386845454,\n\
\ \"acc_norm\": 0.7564766839378239,\n \"acc_norm_stderr\": 0.030975436386845454\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5128205128205128,\n \"acc_stderr\": 0.02534267129380725,\n \
\ \"acc_norm\": 0.5128205128205128,\n \"acc_norm_stderr\": 0.02534267129380725\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5756302521008403,\n \"acc_stderr\": 0.03210479051015776,\n \
\ \"acc_norm\": 0.5756302521008403,\n \"acc_norm_stderr\": 0.03210479051015776\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659806,\n \"\
acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659806\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6935779816513762,\n \"acc_stderr\": 0.01976551722045852,\n \"\
acc_norm\": 0.6935779816513762,\n \"acc_norm_stderr\": 0.01976551722045852\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\
acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7254901960784313,\n \"acc_stderr\": 0.031321798030832904,\n \"\
acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.031321798030832904\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7426160337552743,\n \"acc_stderr\": 0.028458820991460302,\n \
\ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.028458820991460302\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5560538116591929,\n\
\ \"acc_stderr\": 0.03334625674242728,\n \"acc_norm\": 0.5560538116591929,\n\
\ \"acc_norm_stderr\": 0.03334625674242728\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5190839694656488,\n \"acc_stderr\": 0.043820947055509867,\n\
\ \"acc_norm\": 0.5190839694656488,\n \"acc_norm_stderr\": 0.043820947055509867\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\
acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\
\ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n\
\ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6993865030674846,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.6993865030674846,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.045416094465039476,\n\
\ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.045416094465039476\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7863247863247863,\n\
\ \"acc_stderr\": 0.02685345037700916,\n \"acc_norm\": 0.7863247863247863,\n\
\ \"acc_norm_stderr\": 0.02685345037700916\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.6845466155810983,\n\
\ \"acc_stderr\": 0.016617501738763394,\n \"acc_norm\": 0.6845466155810983,\n\
\ \"acc_norm_stderr\": 0.016617501738763394\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.02629622791561367,\n\
\ \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.02629622791561367\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3229050279329609,\n\
\ \"acc_stderr\": 0.015638440380241484,\n \"acc_norm\": 0.3229050279329609,\n\
\ \"acc_norm_stderr\": 0.015638440380241484\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5849673202614379,\n \"acc_stderr\": 0.028213504177824093,\n\
\ \"acc_norm\": 0.5849673202614379,\n \"acc_norm_stderr\": 0.028213504177824093\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6012861736334405,\n\
\ \"acc_stderr\": 0.0278093225857745,\n \"acc_norm\": 0.6012861736334405,\n\
\ \"acc_norm_stderr\": 0.0278093225857745\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5987654320987654,\n \"acc_stderr\": 0.027272582849839796,\n\
\ \"acc_norm\": 0.5987654320987654,\n \"acc_norm_stderr\": 0.027272582849839796\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3900709219858156,\n \"acc_stderr\": 0.029097675599463923,\n \
\ \"acc_norm\": 0.3900709219858156,\n \"acc_norm_stderr\": 0.029097675599463923\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3878748370273794,\n\
\ \"acc_stderr\": 0.012444998309675612,\n \"acc_norm\": 0.3878748370273794,\n\
\ \"acc_norm_stderr\": 0.012444998309675612\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4742647058823529,\n \"acc_stderr\": 0.03033257809455504,\n\
\ \"acc_norm\": 0.4742647058823529,\n \"acc_norm_stderr\": 0.03033257809455504\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4934640522875817,\n \"acc_stderr\": 0.020226106567657807,\n \
\ \"acc_norm\": 0.4934640522875817,\n \"acc_norm_stderr\": 0.020226106567657807\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n\
\ \"acc_stderr\": 0.04709306978661895,\n \"acc_norm\": 0.5909090909090909,\n\
\ \"acc_norm_stderr\": 0.04709306978661895\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6285714285714286,\n \"acc_stderr\": 0.03093285879278986,\n\
\ \"acc_norm\": 0.6285714285714286,\n \"acc_norm_stderr\": 0.03093285879278986\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.746268656716418,\n\
\ \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.746268656716418,\n\
\ \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4457831325301205,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7017543859649122,\n \"acc_stderr\": 0.03508771929824563,\n\
\ \"acc_norm\": 0.7017543859649122,\n \"acc_norm_stderr\": 0.03508771929824563\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3463892288861689,\n\
\ \"mc1_stderr\": 0.01665699710912514,\n \"mc2\": 0.5136120601402686,\n\
\ \"mc2_stderr\": 0.015251718211134593\n }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-c34b-2.2.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|arc:challenge|25_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hellaswag|10_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T18-58-09.868261.parquet'
- config_name: results
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- results_2023-10-01T18-58-09.868261.parquet
- split: latest
path:
- results_2023-10-01T18-58-09.868261.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-c34b-2.2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-c34b-2.2.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [jondurbin/airoboros-c34b-2.2.1](https://huggingface.co/jondurbin/airoboros-c34b-2.2.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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_jondurbin__airoboros-c34b-2.2.1",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-01T18:58:09.868261](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.2.1/blob/main/results_2023-10-01T18-58-09.868261.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": 0.5540698152139825,
"acc_stderr": 0.03493200966396464,
"acc_norm": 0.5578156434457693,
"acc_norm_stderr": 0.03491885315361723,
"mc1": 0.3463892288861689,
"mc1_stderr": 0.01665699710912514,
"mc2": 0.5136120601402686,
"mc2_stderr": 0.015251718211134593
},
"harness|arc:challenge|25": {
"acc": 0.5179180887372014,
"acc_stderr": 0.014602005585490978,
"acc_norm": 0.5469283276450512,
"acc_norm_stderr": 0.014546892052005626
},
"harness|hellaswag|10": {
"acc": 0.5763792073292173,
"acc_stderr": 0.004931219148182242,
"acc_norm": 0.7683728340967935,
"acc_norm_stderr": 0.004210098571170223
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5723684210526315,
"acc_stderr": 0.04026097083296562,
"acc_norm": 0.5723684210526315,
"acc_norm_stderr": 0.04026097083296562
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5358490566037736,
"acc_stderr": 0.030693675018458003,
"acc_norm": 0.5358490566037736,
"acc_norm_stderr": 0.030693675018458003
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5694444444444444,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.5694444444444444,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4624277456647399,
"acc_stderr": 0.0380168510452446,
"acc_norm": 0.4624277456647399,
"acc_norm_stderr": 0.0380168510452446
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3431372549019608,
"acc_stderr": 0.04724007352383889,
"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.04724007352383889
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4851063829787234,
"acc_stderr": 0.032671518489247764,
"acc_norm": 0.4851063829787234,
"acc_norm_stderr": 0.032671518489247764
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.43859649122807015,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.43859649122807015,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370332,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370332
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.025279850397404904,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404904
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.632258064516129,
"acc_stderr": 0.02743086657997347,
"acc_norm": 0.632258064516129,
"acc_norm_stderr": 0.02743086657997347
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4187192118226601,
"acc_stderr": 0.03471192860518468,
"acc_norm": 0.4187192118226601,
"acc_norm_stderr": 0.03471192860518468
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.74,
"acc_stderr": 0.044084400227680794,
"acc_norm": 0.74,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.036085410115739666,
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm_stderr": 0.03395322726375797
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"harness|hendrycksTest-high_school_us_history|5": {
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"acc_norm_stderr": 0.031321798030832904
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_norm_stderr": 0.028458820991460302
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.03334625674242728,
"acc_norm": 0.5560538116591929,
"acc_norm_stderr": 0.03334625674242728
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5190839694656488,
"acc_stderr": 0.043820947055509867,
"acc_norm": 0.5190839694656488,
"acc_norm_stderr": 0.043820947055509867
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7024793388429752,
"acc_stderr": 0.04173349148083499,
"acc_norm": 0.7024793388429752,
"acc_norm_stderr": 0.04173349148083499
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.043733130409147614,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.043733130409147614
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6993865030674846,
"acc_stderr": 0.03602511318806771,
"acc_norm": 0.6993865030674846,
"acc_norm_stderr": 0.03602511318806771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.04726835553719099,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.04726835553719099
},
"harness|hendrycksTest-management|5": {
"acc": 0.6990291262135923,
"acc_stderr": 0.045416094465039476,
"acc_norm": 0.6990291262135923,
"acc_norm_stderr": 0.045416094465039476
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7863247863247863,
"acc_stderr": 0.02685345037700916,
"acc_norm": 0.7863247863247863,
"acc_norm_stderr": 0.02685345037700916
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6845466155810983,
"acc_stderr": 0.016617501738763394,
"acc_norm": 0.6845466155810983,
"acc_norm_stderr": 0.016617501738763394
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6069364161849711,
"acc_stderr": 0.02629622791561367,
"acc_norm": 0.6069364161849711,
"acc_norm_stderr": 0.02629622791561367
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3229050279329609,
"acc_stderr": 0.015638440380241484,
"acc_norm": 0.3229050279329609,
"acc_norm_stderr": 0.015638440380241484
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5849673202614379,
"acc_stderr": 0.028213504177824093,
"acc_norm": 0.5849673202614379,
"acc_norm_stderr": 0.028213504177824093
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6012861736334405,
"acc_stderr": 0.0278093225857745,
"acc_norm": 0.6012861736334405,
"acc_norm_stderr": 0.0278093225857745
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5987654320987654,
"acc_stderr": 0.027272582849839796,
"acc_norm": 0.5987654320987654,
"acc_norm_stderr": 0.027272582849839796
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3900709219858156,
"acc_stderr": 0.029097675599463923,
"acc_norm": 0.3900709219858156,
"acc_norm_stderr": 0.029097675599463923
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3878748370273794,
"acc_stderr": 0.012444998309675612,
"acc_norm": 0.3878748370273794,
"acc_norm_stderr": 0.012444998309675612
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4742647058823529,
"acc_stderr": 0.03033257809455504,
"acc_norm": 0.4742647058823529,
"acc_norm_stderr": 0.03033257809455504
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4934640522875817,
"acc_stderr": 0.020226106567657807,
"acc_norm": 0.4934640522875817,
"acc_norm_stderr": 0.020226106567657807
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5909090909090909,
"acc_stderr": 0.04709306978661895,
"acc_norm": 0.5909090909090909,
"acc_norm_stderr": 0.04709306978661895
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6285714285714286,
"acc_stderr": 0.03093285879278986,
"acc_norm": 0.6285714285714286,
"acc_norm_stderr": 0.03093285879278986
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.746268656716418,
"acc_stderr": 0.03076944496729602,
"acc_norm": 0.746268656716418,
"acc_norm_stderr": 0.03076944496729602
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4457831325301205,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.4457831325301205,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7017543859649122,
"acc_stderr": 0.03508771929824563,
"acc_norm": 0.7017543859649122,
"acc_norm_stderr": 0.03508771929824563
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3463892288861689,
"mc1_stderr": 0.01665699710912514,
"mc2": 0.5136120601402686,
"mc2_stderr": 0.015251718211134593
}
}
```
### 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] |
hztttian/vits-simple-api | 2023-10-01T19:00:46.000Z | [
"license:apache-2.0",
"region:us"
] | hztttian | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
Kilich/affect-visdial | 2023-10-01T20:09:21.000Z | [
"task_categories:text-classification",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"code",
"arxiv:2308.16349",
"region:us"
] | Kilich | null | null | null | 0 | 0 | ---
license: apache-2.0
task_categories:
- text-classification
- text2text-generation
language:
- en
tags:
- code
size_categories:
- 10K<n<100K
---
Dataset Card for "Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
## Dataset Description
- **Homepage:** [affective-visual-dialog.github.io/](https://affective-visual-dialog.github.io/)
- **Repository:** [More Information Needed](https://github.com/Vision-CAIR/affectiveVisDial)
- **Paper:** [More Information Needed](https://arxiv.org/abs/2308.16349)
- **Point of Contact:** [More Information Needed](kilichbek.haydarov@gmail.com)
### Dataset Summary
Affective Visual Dialog is an emotion explanation and reasoning task as a testbed for research on understanding the formation of emotions in visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and (3) Affective emotion explanation generation based on the dialog. Our key contribution is the collection of a large-scale dataset, dubbed AffectVisDial, consisting of 50K 10-turn visually grounded dialogs as well as concluding emotion attributions and dialog-informed textual emotion explanations, resulting in a total of 27,180 working hours. We explain our design decisions in collecting the dataset and introduce the questioner and answerer tasks that are associated with the participants in the conversation. We train and demonstrate solid Affective Visual Dialog baselines adapted from state-of-the-art models. Remarkably, the responses generated by our models show promising emotional reasoning abilities in response to visually grounded conversations.
### Supported Tasks and Leaderboards
We have one task that is available as a challenge:
- [Emotion Explanation Prediction](https://eval.ai/web/challenges/challenge-page/2151/overview)
Challenge have a leaderboard on Eval.ai. Submission deadlines can be viewed from the above links.
In addition, we are hosting the challenge at the ICCV23 workshop [5CLVL]((https://iccv-clvl.github.io/2023/)). We have cash prizes for winners.
### Citation Information
```
@article{haydarov2023affective,
title={Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations},
author={Haydarov, Kilichbek and Shen, Xiaoqian and Madasu, Avinash and Salem, Mahmoud and Li, Jia and Elsayed, Gamaleldin and Elhoseiny, Mohamed},
journal={arXiv preprint arXiv:2308.16349},
year={2023}
}
``` |
AuroraVibe/Toruhh | 2023-10-01T19:46:34.000Z | [
"license:openrail",
"region:us"
] | AuroraVibe | null | null | null | 0 | 0 | ---
license: openrail
---
|
marasama/nva-aurica | 2023-10-01T19:53:39.000Z | [
"region:us"
] | marasama | null | null | null | 0 | 0 | Entry not found |
Roscall/SinatraRVC | 2023-10-01T20:04:56.000Z | [
"region:us"
] | Roscall | null | null | null | 0 | 0 | Entry not found |
SebastianMoncaleano/llama2-4k-cammel | 2023-10-01T20:26:03.000Z | [
"region:us"
] | SebastianMoncaleano | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1847
num_examples: 8
download_size: 3138
dataset_size: 1847
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama2-4k-cammel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AndresLoquendo0/Glou16 | 2023-10-01T20:32:49.000Z | [
"license:openrail",
"region:us"
] | AndresLoquendo0 | null | null | null | 0 | 0 | ---
license: openrail
---
|
Ori/nq-ret-robust | 2023-10-01T20:34:44.000Z | [
"region:us"
] | Ori | null | null | null | 0 | 0 | Entry not found |
Ori/wikihop-ret-robust | 2023-10-01T20:36:04.000Z | [
"region:us"
] | Ori | null | null | null | 0 | 0 | Entry not found |
Ori/strategyqa-ret-robust | 2023-10-01T20:37:10.000Z | [
"region:us"
] | Ori | null | null | null | 0 | 0 | Entry not found |
lukemelas/synthetic-derm | 2023-10-02T01:33:49.000Z | [
"region:us"
] | lukemelas | null | null | null | 0 | 0 | ## Augmenting medical image classifiers with synthetic data from latent diffusion models
Luke W. Sagers*, James A. Diao*, Luke Melas-Kyriazi*, Matthew Groh, Vijaytha Muralidharan, Zhuo Ran Cai, Jesutofunmi A. Omiye, Pranav Rajpurkar, Adewole S. Adamson, Veronica Rotemberg, Roxana Daneshjou, and Arjun K. Manrai
**Abstract:** While hundreds of artificial intelligence (AI) algorithms are now approved or cleared by the US Food and Drugs Administration (FDA), many studies have shown inconsistent generalization or bias, particularly for underrepresented populations. Some have proposed that generative AI could reduce the need for real data, but its utility in model development remains unclear. Skin disease serves as a useful case study in synthetic image generation due to the diversity of disease appearances, particularly across the protected attribute of skin tone. Here we show that latent diffusion models can scalably generate images of skin disease and that augmenting model training with these data improves performance in data-limited settings. These performance gains saturate at synthetic-to-real image ratios above 10:1 and are substantially smaller than the gains obtained from adding real images. We further conducted a human reader study on the synthetic generations, revealing a correlation between physician-assessed photorealism and improvements in model performance. We release a new dataset of 458,920 synthetic images produced using several generation strategies. Our results suggest that synthetic data could serve as a force-multiplier for model development, but the collection of diverse real-world data remains the most important step to improve medical AI algorithms.
### Data
This is the data repository.
### Code
The code and all instructions are available [here](https://github.com/manrai/synthetic-derm).
|
Quilt-AI/ner_reviews | 2023-10-01T21:17:46.000Z | [
"region:us"
] | Quilt-AI | null | null | null | 0 | 0 | Entry not found |
Quilt-AI/Amazon | 2023-10-01T21:46:24.000Z | [
"region:us"
] | Quilt-AI | null | null | null | 0 | 0 | Entry not found |
Quilt-AI/AMZ | 2023-10-01T21:46:39.000Z | [
"region:us"
] | Quilt-AI | null | null | null | 0 | 0 | Entry not found |
AuroraVibe/Toruhhtest | 2023-10-01T21:55:12.000Z | [
"license:openrail",
"region:us"
] | AuroraVibe | null | null | null | 0 | 0 | ---
license: openrail
---
|
HotDaddy/hdnyali | 2023-10-01T22:38:49.000Z | [
"region:us"
] | HotDaddy | null | null | null | 0 | 0 | Entry not found |
Melphin/First-of-paragraph | 2023-10-02T04:10:04.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | Melphin | null | null | null | 0 | 0 | ---
license: cc-by-sa-3.0
---
# First-of-paragraph
First sentence of paragraphs collected from [Random paragraphs](https://www.kaggle.com/datasets/nikitricky/random-paragraphs) dataset
Created by Melphin as an experiment
## Labels
0: Not first sentence
1: First sentence |
ferdIF/ferd-dataset-v2 | 2023-10-02T00:01:26.000Z | [
"region:us"
] | ferdIF | null | null | null | 0 | 0 | Entry not found |
Yang-hugging-face-2023/llama2-verification-1 | 2023-10-02T00:00:06.000Z | [
"region:us"
] | Yang-hugging-face-2023 | null | null | null | 0 | 0 | |
diegomiranda/large-file-test | 2023-10-02T04:07:00.000Z | [
"region:us"
] | diegomiranda | null | null | null | 0 | 0 | teste
|
zen-E/ANLI-simcse-roberta-large-embeddings-pca-256 | 2023-10-03T03:02:21.000Z | [
"task_categories:sentence-similarity",
"size_categories:100K<n<1M",
"language:en",
"region:us"
] | zen-E | null | null | null | 0 | 0 | ---
task_categories:
- sentence-similarity
language:
- en
size_categories:
- 100K<n<1M
---
A dataset that contains all data except those labeled as 'neutral' in 'https://sbert.net/datasets/AllNLI.tsv.gz'' which the corresponding text embedding produced by 'princeton-nlp/unsup-simcse-roberta-large'. The features are transformed to a size of 256 by the PCA object.
In order to load the dictionary of the teacher embeddings corresponding to the anli dataset:
```python
!git clone https://huggingface.co/datasets/zen-E/ANLI-simcse-roberta-large-embeddings-pca-256
# if dimension reduction to 256 is required
import joblib
pca = joblib.load('ANLI-simcse-roberta-large-embeddings-pca-256/pca_model.sav')
teacher_embeddings = torch.load("./ANLI-simcse-roberta-large-embeddings-pca-256/anli_train_simcse_robertra_sent_embed.pt")
if pca is not None:
all_sents = sorted(teacher_embeddings.keys())
teacher_embeddings_values = torch.stack([teacher_embeddings[s] for s in all_sents], dim=0).numpy()
teacher_embeddings_values_trans = pca.transform(teacher_embeddings_values)
teacher_embeddings = {k:torch.tensor(v) for k, v in zip(all_sents, teacher_embeddings_values_trans)}
``` |
rore224/nva-nurimoto | 2023-10-02T00:48:29.000Z | [
"region:us"
] | rore224 | null | null | null | 0 | 0 | Entry not found |
GalacticV/Jasmine | 2023-10-02T00:43:12.000Z | [
"license:openrail",
"region:us"
] | GalacticV | null | null | null | 0 | 0 | ---
license: openrail
---
|
VatsaDev/TinyText | 2023-10-10T18:35:16.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"code",
"region:us"
] | VatsaDev | null | null | null | 5 | 0 | ---
license: mit
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- code
size_categories:
- 100K<n<1M
---
The entire NanoPhi Dataset is at full.jsonl
Separate Tasks Include
- Math (Metamath, mammoth)
- Code (Code Search Net)
- Logic (Open-platypus)
- Roleplay (PIPPA, RoleplayIO)
- Textbooks (Tiny-text, Sciphi)
- Textbook QA (Orca-text, Tiny-webtext) |
open-llm-leaderboard/details_jondurbin__airoboros-l2-70b-2.2.1 | 2023-10-02T00:43:21.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of jondurbin/airoboros-l2-70b-2.2.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-l2-70b-2.2.1](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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_jondurbin__airoboros-l2-70b-2.2.1\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-10-02T00:41:58.859949](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-70b-2.2.1/blob/main/results_2023-10-02T00-41-58.859949.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\": 0.6970834854186557,\n\
\ \"acc_stderr\": 0.031037204423526216,\n \"acc_norm\": 0.7009415944284378,\n\
\ \"acc_norm_stderr\": 0.03100649188026674,\n \"mc1\": 0.4357405140758874,\n\
\ \"mc1_stderr\": 0.017358345398863124,\n \"mc2\": 0.5949086139726426,\n\
\ \"mc2_stderr\": 0.015268616864386245\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6552901023890785,\n \"acc_stderr\": 0.01388881628678211,\n\
\ \"acc_norm\": 0.697098976109215,\n \"acc_norm_stderr\": 0.013428241573185349\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6936865166301533,\n\
\ \"acc_stderr\": 0.004600194559865541,\n \"acc_norm\": 0.8795060744871539,\n\
\ \"acc_norm_stderr\": 0.0032487292211528878\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.031103182383123387,\n\
\ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.031103182383123387\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\
\ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \
\ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337142,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337142\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7986111111111112,\n\
\ \"acc_stderr\": 0.033536474697138406,\n \"acc_norm\": 0.7986111111111112,\n\
\ \"acc_norm_stderr\": 0.033536474697138406\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n\
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105653,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105653\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6808510638297872,\n \"acc_stderr\": 0.030472973363380045,\n\
\ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.030472973363380045\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\
\ \"acc_stderr\": 0.04644602091222318,\n \"acc_norm\": 0.42105263157894735,\n\
\ \"acc_norm_stderr\": 0.04644602091222318\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.040824829046386284,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.040824829046386284\n \
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"\
acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.8387096774193549,\n \"acc_stderr\": 0.0209233270064233,\n\
\ \"acc_norm\": 0.8387096774193549,\n \"acc_norm_stderr\": 0.0209233270064233\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.49261083743842365,\n \"acc_stderr\": 0.03517603540361008,\n \"\
acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.03517603540361008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8585858585858586,\n \"acc_stderr\": 0.024825909793343346,\n \"\
acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.024825909793343346\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078915,\n\
\ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078915\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7564102564102564,\n \"acc_stderr\": 0.021763733684173923,\n\
\ \"acc_norm\": 0.7564102564102564,\n \"acc_norm_stderr\": 0.021763733684173923\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7521008403361344,\n \"acc_stderr\": 0.028047967224176892,\n\
\ \"acc_norm\": 0.7521008403361344,\n \"acc_norm_stderr\": 0.028047967224176892\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4304635761589404,\n \"acc_stderr\": 0.040428099613956346,\n \"\
acc_norm\": 0.4304635761589404,\n \"acc_norm_stderr\": 0.040428099613956346\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8880733944954129,\n \"acc_stderr\": 0.013517352714958788,\n \"\
acc_norm\": 0.8880733944954129,\n \"acc_norm_stderr\": 0.013517352714958788\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6157407407407407,\n \"acc_stderr\": 0.03317354514310742,\n \"\
acc_norm\": 0.6157407407407407,\n \"acc_norm_stderr\": 0.03317354514310742\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813902,\n \"\
acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813902\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8860759493670886,\n \"acc_stderr\": 0.020681745135884565,\n \
\ \"acc_norm\": 0.8860759493670886,\n \"acc_norm_stderr\": 0.020681745135884565\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7802690582959642,\n\
\ \"acc_stderr\": 0.027790177064383595,\n \"acc_norm\": 0.7802690582959642,\n\
\ \"acc_norm_stderr\": 0.027790177064383595\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8320610687022901,\n \"acc_stderr\": 0.032785485373431386,\n\
\ \"acc_norm\": 0.8320610687022901,\n \"acc_norm_stderr\": 0.032785485373431386\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.859504132231405,\n \"acc_stderr\": 0.03172233426002158,\n \"acc_norm\"\
: 0.859504132231405,\n \"acc_norm_stderr\": 0.03172233426002158\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\
\ \"acc_stderr\": 0.035207039905179635,\n \"acc_norm\": 0.8425925925925926,\n\
\ \"acc_norm_stderr\": 0.035207039905179635\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\
\ \"acc_stderr\": 0.01987565502786746,\n \"acc_norm\": 0.8974358974358975,\n\
\ \"acc_norm_stderr\": 0.01987565502786746\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8620689655172413,\n\
\ \"acc_stderr\": 0.012331009307795663,\n \"acc_norm\": 0.8620689655172413,\n\
\ \"acc_norm_stderr\": 0.012331009307795663\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7745664739884393,\n \"acc_stderr\": 0.022497230190967554,\n\
\ \"acc_norm\": 0.7745664739884393,\n \"acc_norm_stderr\": 0.022497230190967554\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5843575418994413,\n\
\ \"acc_stderr\": 0.016482782187500683,\n \"acc_norm\": 0.5843575418994413,\n\
\ \"acc_norm_stderr\": 0.016482782187500683\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7843137254901961,\n \"acc_stderr\": 0.02355083135199509,\n\
\ \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.02355083135199509\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7556270096463023,\n\
\ \"acc_stderr\": 0.02440616209466889,\n \"acc_norm\": 0.7556270096463023,\n\
\ \"acc_norm_stderr\": 0.02440616209466889\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8302469135802469,\n \"acc_stderr\": 0.020888690414093865,\n\
\ \"acc_norm\": 0.8302469135802469,\n \"acc_norm_stderr\": 0.020888690414093865\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5567375886524822,\n \"acc_stderr\": 0.02963483847376601,\n \
\ \"acc_norm\": 0.5567375886524822,\n \"acc_norm_stderr\": 0.02963483847376601\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5534550195567145,\n\
\ \"acc_stderr\": 0.012697046024399656,\n \"acc_norm\": 0.5534550195567145,\n\
\ \"acc_norm_stderr\": 0.012697046024399656\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7463235294117647,\n \"acc_stderr\": 0.026431329870789527,\n\
\ \"acc_norm\": 0.7463235294117647,\n \"acc_norm_stderr\": 0.026431329870789527\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7434640522875817,\n \"acc_stderr\": 0.017667841612379005,\n \
\ \"acc_norm\": 0.7434640522875817,\n \"acc_norm_stderr\": 0.017667841612379005\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8244897959183674,\n \"acc_stderr\": 0.024352800722970015,\n\
\ \"acc_norm\": 0.8244897959183674,\n \"acc_norm_stderr\": 0.024352800722970015\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\
\ \"acc_stderr\": 0.021628920516700637,\n \"acc_norm\": 0.8955223880597015,\n\
\ \"acc_norm_stderr\": 0.021628920516700637\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.025172984350155764,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.025172984350155764\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4357405140758874,\n\
\ \"mc1_stderr\": 0.017358345398863124,\n \"mc2\": 0.5949086139726426,\n\
\ \"mc2_stderr\": 0.015268616864386245\n }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|arc:challenge|25_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hellaswag|10_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T00-41-58.859949.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T00-41-58.859949.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-02T00-41-58.859949.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-02T00-41-58.859949.parquet'
- config_name: results
data_files:
- split: 2023_10_02T00_41_58.859949
path:
- results_2023-10-02T00-41-58.859949.parquet
- split: latest
path:
- results_2023-10-02T00-41-58.859949.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-l2-70b-2.2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [jondurbin/airoboros-l2-70b-2.2.1](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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_jondurbin__airoboros-l2-70b-2.2.1",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-02T00:41:58.859949](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-70b-2.2.1/blob/main/results_2023-10-02T00-41-58.859949.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": 0.6970834854186557,
"acc_stderr": 0.031037204423526216,
"acc_norm": 0.7009415944284378,
"acc_norm_stderr": 0.03100649188026674,
"mc1": 0.4357405140758874,
"mc1_stderr": 0.017358345398863124,
"mc2": 0.5949086139726426,
"mc2_stderr": 0.015268616864386245
},
"harness|arc:challenge|25": {
"acc": 0.6552901023890785,
"acc_stderr": 0.01388881628678211,
"acc_norm": 0.697098976109215,
"acc_norm_stderr": 0.013428241573185349
},
"harness|hellaswag|10": {
"acc": 0.6936865166301533,
"acc_stderr": 0.004600194559865541,
"acc_norm": 0.8795060744871539,
"acc_norm_stderr": 0.0032487292211528878
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.04171654161354543,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.04171654161354543
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8223684210526315,
"acc_stderr": 0.031103182383123387,
"acc_norm": 0.8223684210526315,
"acc_norm_stderr": 0.031103182383123387
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.027943219989337142,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.027943219989337142
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7986111111111112,
"acc_stderr": 0.033536474697138406,
"acc_norm": 0.7986111111111112,
"acc_norm_stderr": 0.033536474697138406
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105653,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105653
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6808510638297872,
"acc_stderr": 0.030472973363380045,
"acc_norm": 0.6808510638297872,
"acc_norm_stderr": 0.030472973363380045
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.04644602091222318,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.04644602091222318
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6,
"acc_stderr": 0.040824829046386284,
"acc_norm": 0.6,
"acc_norm_stderr": 0.040824829046386284
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.025487187147859375,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.025487187147859375
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5079365079365079,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.5079365079365079,
"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8387096774193549,
"acc_stderr": 0.0209233270064233,
"acc_norm": 0.8387096774193549,
"acc_norm_stderr": 0.0209233270064233
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.49261083743842365,
"acc_stderr": 0.03517603540361008,
"acc_norm": 0.49261083743842365,
"acc_norm_stderr": 0.03517603540361008
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8,
"acc_stderr": 0.031234752377721175,
"acc_norm": 0.8,
"acc_norm_stderr": 0.031234752377721175
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8585858585858586,
"acc_stderr": 0.024825909793343346,
"acc_norm": 0.8585858585858586,
"acc_norm_stderr": 0.024825909793343346
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9326424870466321,
"acc_stderr": 0.018088393839078915,
"acc_norm": 0.9326424870466321,
"acc_norm_stderr": 0.018088393839078915
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7564102564102564,
"acc_stderr": 0.021763733684173923,
"acc_norm": 0.7564102564102564,
"acc_norm_stderr": 0.021763733684173923
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7521008403361344,
"acc_stderr": 0.028047967224176892,
"acc_norm": 0.7521008403361344,
"acc_norm_stderr": 0.028047967224176892
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4304635761589404,
"acc_stderr": 0.040428099613956346,
"acc_norm": 0.4304635761589404,
"acc_norm_stderr": 0.040428099613956346
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8880733944954129,
"acc_stderr": 0.013517352714958788,
"acc_norm": 0.8880733944954129,
"acc_norm_stderr": 0.013517352714958788
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6157407407407407,
"acc_stderr": 0.03317354514310742,
"acc_norm": 0.6157407407407407,
"acc_norm_stderr": 0.03317354514310742
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9166666666666666,
"acc_stderr": 0.019398452135813902,
"acc_norm": 0.9166666666666666,
"acc_norm_stderr": 0.019398452135813902
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8860759493670886,
"acc_stderr": 0.020681745135884565,
"acc_norm": 0.8860759493670886,
"acc_norm_stderr": 0.020681745135884565
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7802690582959642,
"acc_stderr": 0.027790177064383595,
"acc_norm": 0.7802690582959642,
"acc_norm_stderr": 0.027790177064383595
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8320610687022901,
"acc_stderr": 0.032785485373431386,
"acc_norm": 0.8320610687022901,
"acc_norm_stderr": 0.032785485373431386
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.859504132231405,
"acc_stderr": 0.03172233426002158,
"acc_norm": 0.859504132231405,
"acc_norm_stderr": 0.03172233426002158
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8425925925925926,
"acc_stderr": 0.035207039905179635,
"acc_norm": 0.8425925925925926,
"acc_norm_stderr": 0.035207039905179635
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.031921934489347235,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.031921934489347235
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5357142857142857,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.5357142857142857,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.8349514563106796,
"acc_stderr": 0.03675668832233188,
"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.03675668832233188
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8974358974358975,
"acc_stderr": 0.01987565502786746,
"acc_norm": 0.8974358974358975,
"acc_norm_stderr": 0.01987565502786746
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8620689655172413,
"acc_stderr": 0.012331009307795663,
"acc_norm": 0.8620689655172413,
"acc_norm_stderr": 0.012331009307795663
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7745664739884393,
"acc_stderr": 0.022497230190967554,
"acc_norm": 0.7745664739884393,
"acc_norm_stderr": 0.022497230190967554
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.5843575418994413,
"acc_stderr": 0.016482782187500683,
"acc_norm": 0.5843575418994413,
"acc_norm_stderr": 0.016482782187500683
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7843137254901961,
"acc_stderr": 0.02355083135199509,
"acc_norm": 0.7843137254901961,
"acc_norm_stderr": 0.02355083135199509
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7556270096463023,
"acc_stderr": 0.02440616209466889,
"acc_norm": 0.7556270096463023,
"acc_norm_stderr": 0.02440616209466889
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8302469135802469,
"acc_stderr": 0.020888690414093865,
"acc_norm": 0.8302469135802469,
"acc_norm_stderr": 0.020888690414093865
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5567375886524822,
"acc_stderr": 0.02963483847376601,
"acc_norm": 0.5567375886524822,
"acc_norm_stderr": 0.02963483847376601
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5534550195567145,
"acc_stderr": 0.012697046024399656,
"acc_norm": 0.5534550195567145,
"acc_norm_stderr": 0.012697046024399656
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7463235294117647,
"acc_stderr": 0.026431329870789527,
"acc_norm": 0.7463235294117647,
"acc_norm_stderr": 0.026431329870789527
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7434640522875817,
"acc_stderr": 0.017667841612379005,
"acc_norm": 0.7434640522875817,
"acc_norm_stderr": 0.017667841612379005
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.043091187099464585,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.043091187099464585
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8244897959183674,
"acc_stderr": 0.024352800722970015,
"acc_norm": 0.8244897959183674,
"acc_norm_stderr": 0.024352800722970015
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8955223880597015,
"acc_stderr": 0.021628920516700637,
"acc_norm": 0.8955223880597015,
"acc_norm_stderr": 0.021628920516700637
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.92,
"acc_stderr": 0.0272659924344291,
"acc_norm": 0.92,
"acc_norm_stderr": 0.0272659924344291
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.025172984350155764,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.025172984350155764
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4357405140758874,
"mc1_stderr": 0.017358345398863124,
"mc2": 0.5949086139726426,
"mc2_stderr": 0.015268616864386245
}
}
```
### 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] |
GalacticV/Aria_test | 2023-10-02T01:18:16.000Z | [
"license:openrail",
"region:us"
] | GalacticV | null | null | null | 0 | 0 | ---
license: openrail
---
|
Glasshes/DebateV4.1 | 2023-10-02T01:16:16.000Z | [
"region:us"
] | Glasshes | null | null | null | 0 | 0 | Entry not found |
aghent/copiapoa-augmented | 2023-10-02T01:49:47.000Z | [
"license:apache-2.0",
"region:us"
] | aghent | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
BangumiBase/tsuredurechildren | 2023-10-02T02:21:40.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Tsuredure Children
This is the image base of bangumi Tsuredure Children, we detected 25 characters, 1139 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 89 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 88 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 109 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 62 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 94 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 15 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 85 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 29 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 36 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 34 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 6 | [Download](10/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 11 | 30 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 71 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 14 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 9 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 12 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 39 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 26 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 21 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 51 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 47 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 81 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 15 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 23 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 53 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
DioniSilva/forklifts-data | 2023-10-02T01:40:58.000Z | [
"region:us"
] | DioniSilva | null | null | null | 0 | 0 | Entry not found |
TKNodven/Mordred | 2023-10-02T01:45:19.000Z | [
"language:ja",
"region:us"
] | TKNodven | null | null | null | 0 | 0 | ---
language:
- ja
--- |
TKNodven/Mordredvoice2 | 2023-10-02T14:55:23.000Z | [
"license:openrail",
"region:us"
] | TKNodven | null | null | null | 0 | 0 | ---
license: openrail
---
|
BangumiBase/gotoubunnohanayome | 2023-10-02T03:34:42.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Gotoubun No Hanayome
This is the image base of bangumi Gotoubun no Hanayome, we detected 30 characters, 3251 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 361 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 21 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 347 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 352 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 51 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 29 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 385 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 305 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 12 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 32 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 707 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 152 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 41 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 29 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 11 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 13 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 18 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 27 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 23 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 12 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 9 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 15 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 11 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 12 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 5 | [Download](24/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 25 | 27 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 17 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 9 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 208 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/chuunibyoudemokoigashitai | 2023-10-02T04:38:56.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Chuunibyou Demo Koi Ga Shitai!
This is the image base of bangumi Chuunibyou demo Koi ga Shitai!, we detected 37 characters, 5023 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1250 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 87 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 26 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 47 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 307 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 1197 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 74 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 84 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 23 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 91 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 16 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 171 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 50 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 14 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 17 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 491 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 27 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 89 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 23 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 17 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 39 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 377 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 13 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 25 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 19 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 10 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 9 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 7 | [Download](28/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 29 | 8 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 17 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 20 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 10 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 12 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 6 | [Download](34/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 35 | 10 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 333 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
GalacticV/Aria_2 | 2023-10-02T21:52:52.000Z | [
"license:openrail",
"region:us"
] | GalacticV | null | null | null | 0 | 0 | ---
license: openrail
---
|
lexaizero/AingMaungArrghhhAingSiaMAungEtahSaha | 2023-10-02T02:23:20.000Z | [
"license:mit",
"region:us"
] | lexaizero | null | null | null | 0 | 0 | ---
license: mit
---
|
BangumiBase/eromangasensei | 2023-10-02T03:40:48.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Eromanga-sensei
This is the image base of bangumi Eromanga-sensei, we detected 16 characters, 1936 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 732 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 39 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 34 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 11 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 33 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 302 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 51 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 15 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 81 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 166 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 34 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 257 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 30 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 7 | [Download](13/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 14 | 13 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 131 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
kanoayu/nva-kud | 2023-10-02T22:12:51.000Z | [
"region:us"
] | kanoayu | null | null | null | 0 | 0 | Entry not found |
Qrul/ArulAI | 2023-10-02T02:59:13.000Z | [
"region:us"
] | Qrul | null | null | null | 0 | 0 | Entry not found |
Sekais/yy | 2023-10-02T03:26:18.000Z | [
"license:openrail",
"region:us"
] | Sekais | null | null | null | 0 | 0 | ---
license: openrail
---
|
wu981526092/Stereotype-Elicitation-Prompt-Library | 2023-10-02T03:11:05.000Z | [
"license:mit",
"region:us"
] | wu981526092 | null | null | null | 0 | 0 | ---
license: mit
---
|
lodestones/potato | 2023-10-02T04:30:25.000Z | [
"license:wtfpl",
"region:us"
] | lodestones | null | null | null | 0 | 0 | ---
license: wtfpl
---
|
chunpingvi/dataset_format1 | 2023-10-02T03:23:38.000Z | [
"region:us"
] | chunpingvi | null | null | null | 0 | 0 | Entry not found |
ptx0/mj-general | 2023-10-09T03:33:41.000Z | [
"task_categories:feature-extraction",
"task_categories:image-to-image",
"task_categories:text-to-image",
"size_categories:1M<n<10M",
"license:afl-3.0",
"region:us"
] | ptx0 | null | null | null | 1 | 0 | ---
license: afl-3.0
task_categories:
- feature-extraction
- image-to-image
- text-to-image
pretty_name: Midjourney 5.2-centric Data
size_categories:
- 1M<n<10M
---
All 20 of Midjourney's #general channels scraped over the course of a week.
Roughly 5 million clean captions of individual images.
The `original_data` branch/revision contains all of the original data before cleanup, including tiled images.
The `version` column of the dataset tells you which model it was created with. Midjourney uses v5.2 by default. |
dakadkart/Clow | 2023-10-02T04:23:20.000Z | [
"region:us"
] | dakadkart | null | null | null | 0 | 0 | Entry not found |
zhan1993/topic_instructions | 2023-10-02T05:50:21.000Z | [
"region:us"
] | zhan1993 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
- name: subject
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 7371066
num_examples: 20331
download_size: 4139736
dataset_size: 7371066
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "topic_instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
poorguys/chinese_fonts_basic_512x512 | 2023-10-02T04:57:48.000Z | [
"region:us"
] | poorguys | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: image
dtype: image
- name: char
dtype: string
- name: unicode
dtype: string
- name: font
dtype: string
- name: font_type
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 12023141.0
num_examples: 973
download_size: 6569035
dataset_size: 12023141.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "chinese_fonts_basic_512x512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jsrdhher/videojs | 2023-10-10T10:53:50.000Z | [
"region:us"
] | jsrdhher | null | null | null | 0 | 0 | Entry not found |
BangumiBase/shokeishoujonovirginroad | 2023-10-02T05:57:06.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Shokei Shoujo No Virgin Road
This is the image base of bangumi Shokei Shoujo no Virgin Road, we detected 18 characters, 1105 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 7 | [Download](0/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 1 | 19 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 24 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 11 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 30 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 10 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 37 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 228 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 30 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 44 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 76 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 34 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 25 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 49 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 266 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 79 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 6 | [Download](16/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 130 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
poorguys/chinese_fonts_common_512x512 | 2023-10-02T06:51:37.000Z | [
"region:us"
] | poorguys | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: char
dtype: string
- name: unicode
dtype: string
- name: font
dtype: string
- name: font_type
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8824296546.625
num_examples: 446299
download_size: 2158621665
dataset_size: 8824296546.625
---
# Dataset Card for "chinese_fonts_common_512x512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Silvester23/kenzok | 2023-10-02T05:22:44.000Z | [
"region:us"
] | Silvester23 | null | null | null | 0 | 0 | Entry not found |
mesolitica/embedding-pair-mining | 2023-10-02T06:08:13.000Z | [
"region:us"
] | mesolitica | null | null | null | 0 | 0 | Entry not found |
Adiguna/Misbah | 2023-10-02T05:54:54.000Z | [
"region:us"
] | Adiguna | null | null | null | 0 | 0 | Entry not found |
hejxj/wtfpl | 2023-10-02T06:45:25.000Z | [
"license:wtfpl",
"region:us"
] | hejxj | null | null | null | 0 | 0 | ---
license: wtfpl
---
|
hztang/GPT5000 | 2023-10-02T06:58:23.000Z | [
"region:us"
] | hztang | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 14950932.581919776
num_examples: 4008
- name: test
num_bytes: 3741463.4180802237
num_examples: 1003
download_size: 10699351
dataset_size: 18692396.0
---
# Dataset Card for "GPT5000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hztang/GPTV3 | 2023-10-02T07:00:23.000Z | [
"region:us"
] | hztang | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: PMC
dtype: string
splits:
- name: train
num_bytes: 15024534.690880064
num_examples: 4008
- name: validation
num_bytes: 1878066.836360008
num_examples: 501
- name: test
num_bytes: 1881815.472759928
num_examples: 502
download_size: 10731439
dataset_size: 18784417.0
---
# Dataset Card for "GPTV3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yagnikposhiya/CommonVoiceCorpusPunjabi15 | 2023-10-02T08:20:16.000Z | [
"language:pa",
"license:apache-2.0",
"region:us"
] | yagnikposhiya | null | null | null | 0 | 0 | ---
license: apache-2.0
language:
- pa
---
## ComonVoiceCorpusPunjabi15
#### Directory structure:
1. **assets**<br>
**a.** Download whole compressed dataset by clicking on the cv-corpus-15.0-2023-09-08-pa-IN.tar.gz file.<br>
2. **data**<br>
**a.** All audio files are stored into "clips" directory<br>
**b.** All metadata are also stored into "data" directory
3. **Credit:** [Common Voice moz://a](https://commonvoice.mozilla.org/hi/datasets)
|
ashley-ng/bandori-cards | 2023-10-10T02:16:08.000Z | [
"region:us"
] | ashley-ng | null | null | null | 0 | 0 | - **Raw images** (cards.tar.gz): contains 2160 highres images of rarities 3, 4, 5 scraped from bandori.party as of 2023-10-02
- **Processed cards** (bandori-card-processed.tar.gz): cropped using three-stage cropping technique introduced in [waifuc](https://github.com/deepghs/waifuc/tree/main).
Contains 628 full-body images (non-trained cards only), 422 half-body images, and 46 head (face close-up) images. Only keep cards with the old artstyle. |
Elain-q/dolma_low_quality_data | 2023-10-03T02:31:58.000Z | [
"license:apache-2.0",
"region:us"
] | Elain-q | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
ormeshein/captcha | 2023-10-02T08:17:41.000Z | [
"license:mit",
"region:us"
] | ormeshein | null | null | null | 0 | 0 | ---
license: mit
---
|
philschmid/neuronx-docs-2-14 | 2023-10-02T08:15:55.000Z | [
"region:us"
] | philschmid | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: title
dtype: string
- name: url
dtype: string
- name: markdown
dtype: string
- name: html
dtype: string
- name: crawlDate
dtype: string
splits:
- name: train
num_bytes: 67513723
num_examples: 913
download_size: 14721061
dataset_size: 67513723
---
# Dataset Card for "neuronx-docs-2-14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mesolitica/google-translate-commitpackft | 2023-10-02T08:24:04.000Z | [
"region:us"
] | mesolitica | null | null | null | 0 | 0 | Entry not found |
mhdkarbik91/Tt | 2023-10-02T08:32:55.000Z | [
"region:us"
] | mhdkarbik91 | null | null | null | 0 | 0 | Entry not found |
nikchar/retrieval_verification_bm25_squeezebert_v2 | 2023-10-02T08:37:24.000Z | [
"region:us"
] | nikchar | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: claim
dtype: string
- name: evidence_wiki_url
dtype: string
- name: text
dtype: string
- name: retrieved_evidence_title
sequence: string
- name: retrieved_evidence_text
sequence: string
- name: labels
dtype: int64
- name: Retrieval_Success
dtype: bool
- name: Predicted_Labels
dtype: int64
- name: Predicted_Labels_Each_doc
sequence: int64
splits:
- name: train
num_bytes: 66031496
num_examples: 11073
download_size: 30811918
dataset_size: 66031496
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "retrieval_verification_bm25_squeezebert_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bybid/i | 2023-10-02T08:51:56.000Z | [
"region:us"
] | bybid | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_KnutJaegersberg__RWKV-pileplus-1B5-evol_instruct_v2 | 2023-10-02T09:02:17.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2](https://huggingface.co/KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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_KnutJaegersberg__RWKV-pileplus-1B5-evol_instruct_v2\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-10-02T09:01:04.742783](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__RWKV-pileplus-1B5-evol_instruct_v2/blob/main/results_2023-10-02T09-01-04.742783.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\": 0.25494922839990974,\n\
\ \"acc_stderr\": 0.0314010444904469,\n \"acc_norm\": 0.25761849126230174,\n\
\ \"acc_norm_stderr\": 0.03140672184687226,\n \"mc1\": 0.21542227662178703,\n\
\ \"mc1_stderr\": 0.014391902652427678,\n \"mc2\": 0.3521451447553084,\n\
\ \"mc2_stderr\": 0.013530448314563733\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.2935153583617747,\n \"acc_stderr\": 0.013307250444941125,\n\
\ \"acc_norm\": 0.318259385665529,\n \"acc_norm_stderr\": 0.013611993916971453\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4223262298346943,\n\
\ \"acc_stderr\": 0.0049292048643159725,\n \"acc_norm\": 0.5550687114120693,\n\
\ \"acc_norm_stderr\": 0.004959425421382027\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3111111111111111,\n\
\ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.3111111111111111,\n\
\ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.03459777606810534,\n\
\ \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.03459777606810534\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\": 0.19622641509433963,\n \"acc_stderr\": 0.024442388131100824,\n\
\ \"acc_norm\": 0.19622641509433963,\n \"acc_norm_stderr\": 0.024442388131100824\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\
\ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.24305555555555555,\n\
\ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.15,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n\
\ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24855491329479767,\n\
\ \"acc_stderr\": 0.03295304696818317,\n \"acc_norm\": 0.24855491329479767,\n\
\ \"acc_norm_stderr\": 0.03295304696818317\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.12745098039215685,\n \"acc_stderr\": 0.033182249219420756,\n\
\ \"acc_norm\": 0.12745098039215685,\n \"acc_norm_stderr\": 0.033182249219420756\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.23,\n\
\ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.03013590647851756,\n\
\ \"acc_norm\": 0.30638297872340425,\n \"acc_norm_stderr\": 0.03013590647851756\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.22807017543859648,\n\
\ \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n\
\ \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135303,\n\
\ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135303\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2671957671957672,\n \"acc_stderr\": 0.02278967314577656,\n \"\
acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.02278967314577656\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.19047619047619047,\n\
\ \"acc_stderr\": 0.03512207412302052,\n \"acc_norm\": 0.19047619047619047,\n\
\ \"acc_norm_stderr\": 0.03512207412302052\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\": 0.2709677419354839,\n\
\ \"acc_stderr\": 0.025284416114900156,\n \"acc_norm\": 0.2709677419354839,\n\
\ \"acc_norm_stderr\": 0.025284416114900156\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2512315270935961,\n \"acc_stderr\": 0.030516530732694436,\n\
\ \"acc_norm\": 0.2512315270935961,\n \"acc_norm_stderr\": 0.030516530732694436\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\"\
: 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2727272727272727,\n \"acc_stderr\": 0.03477691162163659,\n\
\ \"acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.03477691162163659\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.15656565656565657,\n \"acc_stderr\": 0.025890520358141454,\n \"\
acc_norm\": 0.15656565656565657,\n \"acc_norm_stderr\": 0.025890520358141454\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.24870466321243523,\n \"acc_stderr\": 0.031195840877700304,\n\
\ \"acc_norm\": 0.24870466321243523,\n \"acc_norm_stderr\": 0.031195840877700304\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2230769230769231,\n \"acc_stderr\": 0.02110773012724399,\n \
\ \"acc_norm\": 0.2230769230769231,\n \"acc_norm_stderr\": 0.02110773012724399\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \
\ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.026265024608275882,\n\
\ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.026265024608275882\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.19205298013245034,\n \"acc_stderr\": 0.032162984205936156,\n \"\
acc_norm\": 0.19205298013245034,\n \"acc_norm_stderr\": 0.032162984205936156\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.22018348623853212,\n \"acc_stderr\": 0.01776597865232755,\n \"\
acc_norm\": 0.22018348623853212,\n \"acc_norm_stderr\": 0.01776597865232755\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.30092592592592593,\n \"acc_stderr\": 0.03128039084329882,\n \"\
acc_norm\": 0.30092592592592593,\n \"acc_norm_stderr\": 0.03128039084329882\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.27941176470588236,\n \"acc_stderr\": 0.031493281045079556,\n \"\
acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.031493281045079556\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.27848101265822783,\n \"acc_stderr\": 0.02917868230484253,\n \
\ \"acc_norm\": 0.27848101265822783,\n \"acc_norm_stderr\": 0.02917868230484253\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2556053811659193,\n\
\ \"acc_stderr\": 0.029275891003969927,\n \"acc_norm\": 0.2556053811659193,\n\
\ \"acc_norm_stderr\": 0.029275891003969927\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.38016528925619836,\n \"acc_stderr\": 0.04431324501968432,\n \"\
acc_norm\": 0.38016528925619836,\n \"acc_norm_stderr\": 0.04431324501968432\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.17592592592592593,\n\
\ \"acc_stderr\": 0.03680918141673881,\n \"acc_norm\": 0.17592592592592593,\n\
\ \"acc_norm_stderr\": 0.03680918141673881\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n\
\ \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n\
\ \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n\
\ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.28205128205128205,\n\
\ \"acc_stderr\": 0.029480360549541194,\n \"acc_norm\": 0.28205128205128205,\n\
\ \"acc_norm_stderr\": 0.029480360549541194\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2515964240102171,\n\
\ \"acc_stderr\": 0.015517322365529619,\n \"acc_norm\": 0.2515964240102171,\n\
\ \"acc_norm_stderr\": 0.015517322365529619\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.30057803468208094,\n \"acc_stderr\": 0.0246853168672578,\n\
\ \"acc_norm\": 0.30057803468208094,\n \"acc_norm_stderr\": 0.0246853168672578\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2223463687150838,\n\
\ \"acc_stderr\": 0.013907189208156881,\n \"acc_norm\": 0.2223463687150838,\n\
\ \"acc_norm_stderr\": 0.013907189208156881\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.24836601307189543,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.24836601307189543,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2797427652733119,\n\
\ \"acc_stderr\": 0.025494259350694888,\n \"acc_norm\": 0.2797427652733119,\n\
\ \"acc_norm_stderr\": 0.025494259350694888\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2623456790123457,\n \"acc_stderr\": 0.024477222856135118,\n\
\ \"acc_norm\": 0.2623456790123457,\n \"acc_norm_stderr\": 0.024477222856135118\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2907801418439716,\n \"acc_stderr\": 0.027090664368353178,\n \
\ \"acc_norm\": 0.2907801418439716,\n \"acc_norm_stderr\": 0.027090664368353178\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.27183833116036504,\n\
\ \"acc_stderr\": 0.011363135278651411,\n \"acc_norm\": 0.27183833116036504,\n\
\ \"acc_norm_stderr\": 0.011363135278651411\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.19852941176470587,\n \"acc_stderr\": 0.02423101337054109,\n\
\ \"acc_norm\": 0.19852941176470587,\n \"acc_norm_stderr\": 0.02423101337054109\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2777777777777778,\n \"acc_stderr\": 0.01812022425148458,\n \
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.01812022425148458\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2636363636363636,\n\
\ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.2636363636363636,\n\
\ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.22857142857142856,\n \"acc_stderr\": 0.026882144922307744,\n\
\ \"acc_norm\": 0.22857142857142856,\n \"acc_norm_stderr\": 0.026882144922307744\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23383084577114427,\n\
\ \"acc_stderr\": 0.029929415408348384,\n \"acc_norm\": 0.23383084577114427,\n\
\ \"acc_norm_stderr\": 0.029929415408348384\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \
\ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3132530120481928,\n\
\ \"acc_stderr\": 0.03610805018031023,\n \"acc_norm\": 0.3132530120481928,\n\
\ \"acc_norm_stderr\": 0.03610805018031023\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.034462962170884265,\n\
\ \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.034462962170884265\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.21542227662178703,\n\
\ \"mc1_stderr\": 0.014391902652427678,\n \"mc2\": 0.3521451447553084,\n\
\ \"mc2_stderr\": 0.013530448314563733\n }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2
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_10_02T09_01_04.742783
path:
- '**/details_harness|arc:challenge|25_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hellaswag|10_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T09-01-04.742783.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-02T09-01-04.742783.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-02T09-01-04.742783.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-02T09-01-04.742783.parquet'
- config_name: results
data_files:
- split: 2023_10_02T09_01_04.742783
path:
- results_2023-10-02T09-01-04.742783.parquet
- split: latest
path:
- results_2023-10-02T09-01-04.742783.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2
- **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 [KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2](https://huggingface.co/KnutJaegersberg/RWKV-pileplus-1B5-evol_instruct_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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_KnutJaegersberg__RWKV-pileplus-1B5-evol_instruct_v2",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-02T09:01:04.742783](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__RWKV-pileplus-1B5-evol_instruct_v2/blob/main/results_2023-10-02T09-01-04.742783.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": 0.25494922839990974,
"acc_stderr": 0.0314010444904469,
"acc_norm": 0.25761849126230174,
"acc_norm_stderr": 0.03140672184687226,
"mc1": 0.21542227662178703,
"mc1_stderr": 0.014391902652427678,
"mc2": 0.3521451447553084,
"mc2_stderr": 0.013530448314563733
},
"harness|arc:challenge|25": {
"acc": 0.2935153583617747,
"acc_stderr": 0.013307250444941125,
"acc_norm": 0.318259385665529,
"acc_norm_stderr": 0.013611993916971453
},
"harness|hellaswag|10": {
"acc": 0.4223262298346943,
"acc_stderr": 0.0049292048643159725,
"acc_norm": 0.5550687114120693,
"acc_norm_stderr": 0.004959425421382027
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3111111111111111,
"acc_stderr": 0.039992628766177214,
"acc_norm": 0.3111111111111111,
"acc_norm_stderr": 0.039992628766177214
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.03459777606810534,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.03459777606810534
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.21,
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"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.19622641509433963,
"acc_stderr": 0.024442388131100824,
"acc_norm": 0.19622641509433963,
"acc_norm_stderr": 0.024442388131100824
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.24305555555555555,
"acc_norm_stderr": 0.03586879280080341
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.15,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_stderr": 0.03295304696818317,
"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.03295304696818317
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.12745098039215685,
"acc_norm_stderr": 0.033182249219420756
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.30638297872340425,
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"acc_norm": 0.30638297872340425,
"acc_norm_stderr": 0.03013590647851756
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.22807017543859648,
"acc_stderr": 0.03947152782669415,
"acc_norm": 0.22807017543859648,
"acc_norm_stderr": 0.03947152782669415
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.03565998174135303,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.03565998174135303
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.2671957671957672,
"acc_norm_stderr": 0.02278967314577656
},
"harness|hendrycksTest-formal_logic|5": {
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.31,
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},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.2512315270935961,
"acc_norm_stderr": 0.030516530732694436
},
"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm_stderr": 0.03477691162163659
},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.24870466321243523,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.28888888888888886,
"acc_norm_stderr": 0.027634907264178544
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.20588235294117646,
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"acc_norm": 0.20588235294117646,
"acc_norm_stderr": 0.026265024608275882
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.19205298013245034,
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"acc_norm": 0.19205298013245034,
"acc_norm_stderr": 0.032162984205936156
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.22018348623853212,
"acc_stderr": 0.01776597865232755,
"acc_norm": 0.22018348623853212,
"acc_norm_stderr": 0.01776597865232755
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.30092592592592593,
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"acc_norm": 0.30092592592592593,
"acc_norm_stderr": 0.03128039084329882
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.27941176470588236,
"acc_stderr": 0.031493281045079556,
"acc_norm": 0.27941176470588236,
"acc_norm_stderr": 0.031493281045079556
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.27848101265822783,
"acc_stderr": 0.02917868230484253,
"acc_norm": 0.27848101265822783,
"acc_norm_stderr": 0.02917868230484253
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.029275891003969927,
"acc_norm": 0.2556053811659193,
"acc_norm_stderr": 0.029275891003969927
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.22900763358778625,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.22900763358778625,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.38016528925619836,
"acc_stderr": 0.04431324501968432,
"acc_norm": 0.38016528925619836,
"acc_norm_stderr": 0.04431324501968432
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.17592592592592593,
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"acc_norm": 0.17592592592592593,
"acc_norm_stderr": 0.03680918141673881
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.25766871165644173,
"acc_norm_stderr": 0.03436150827846917
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.30357142857142855,
"acc_stderr": 0.04364226155841044,
"acc_norm": 0.30357142857142855,
"acc_norm_stderr": 0.04364226155841044
},
"harness|hendrycksTest-management|5": {
"acc": 0.2524271844660194,
"acc_stderr": 0.04301250399690877,
"acc_norm": 0.2524271844660194,
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},
"harness|hendrycksTest-marketing|5": {
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"acc_stderr": 0.029480360549541194,
"acc_norm": 0.28205128205128205,
"acc_norm_stderr": 0.029480360549541194
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.28,
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.30057803468208094,
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"acc_norm": 0.30057803468208094,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2223463687150838,
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"acc_norm": 0.2223463687150838,
"acc_norm_stderr": 0.013907189208156881
},
"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.2797427652733119,
"acc_norm_stderr": 0.025494259350694888
},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.029929415408348384
},
"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.3132530120481928,
"acc_norm_stderr": 0.03610805018031023
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.034462962170884265,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.034462962170884265
},
"harness|truthfulqa:mc|0": {
"mc1": 0.21542227662178703,
"mc1_stderr": 0.014391902652427678,
"mc2": 0.3521451447553084,
"mc2_stderr": 0.013530448314563733
}
}
```
### 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] |
BangumiBase/sorayorimotooibasho | 2023-10-02T10:25:41.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Sora Yori Mo Tooi Basho
This is the image base of bangumi Sora yori mo Tooi Basho, we detected 20 characters, 2192 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 445 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 76 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 359 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 94 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 220 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 62 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 111 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 301 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 101 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 23 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 13 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 16 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 72 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 19 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 48 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 56 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 70 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 19 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 12 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 75 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
DynamicSuperb/Dummy_for_Attack | 2023-10-04T02:16:44.000Z | [
"license:apache-2.0",
"region:us"
] | DynamicSuperb | null | null | null | 0 | 0 | ---
license: apache-2.0
---
|
BangumiBase/yuruyuri | 2023-10-02T11:40:58.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Yuru Yuri
This is the image base of bangumi Yuru Yuri, we detected 31 characters, 5219 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 45 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 429 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 55 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 680 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 54 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 26 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 431 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 443 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 334 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 22 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 103 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 331 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 25 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 14 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 21 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 10 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 324 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 70 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 23 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 19 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 9 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 16 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 39 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 918 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 449 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 22 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 20 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 19 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 21 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 232 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Coroseven/YuukiAsuna | 2023-10-02T09:40:26.000Z | [
"region:us"
] | Coroseven | null | null | null | 0 | 0 | Entry not found |
riltomagola19/umma-jadi | 2023-10-02T09:45:08.000Z | [
"region:us"
] | riltomagola19 | null | null | null | 0 | 0 | Entry not found |
z5208980/fhir-summarizer | 2023-10-02T09:51:15.000Z | [
"region:us"
] | z5208980 | null | null | null | 0 | 0 | Entry not found |
yagnikposhiya/CommonVoiceCorpusBengali15 | 2023-10-02T10:53:09.000Z | [
"language:bn",
"license:apache-2.0",
"region:us"
] | yagnikposhiya | null | null | null | 0 | 0 | ---
license: apache-2.0
language:
- bn
--- |
soratobtai/decaf | 2023-10-02T10:03:35.000Z | [
"license:mit",
"region:us"
] | soratobtai | null | null | null | 0 | 0 | ---
license: mit
---
|
BangumiBase/fatezero | 2023-10-02T11:59:05.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Fate/zero
This is the image base of bangumi Fate/Zero, we detected 26 characters, 2067 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 145 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 14 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 244 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 109 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 285 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 151 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 71 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 40 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 36 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 70 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 27 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 16 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 14 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 23 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 16 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 167 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 72 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 59 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 34 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 9 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 286 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 17 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 25 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 20 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 6 | [Download](24/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 111 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Sviluppo/openassistance-oaservice | 2023-10-02T12:55:36.000Z | [
"region:us"
] | Sviluppo | null | null | null | 0 | 0 | Entry not found |
Mxode/StackOverflow-QA-C-Language-40k | 2023-10-02T10:30:22.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | Mxode | null | null | null | 1 | 0 | ---
license: apache-2.0
language:
- en
tags:
- code
task_categories:
- question-answering
size_categories:
- 10K<n<100K
---
This is a collection of ~40k QA's in **C Language** from StackOverflow. The data has been initially cleaned, and each response is with **Accepted Answer**.
All data is **<1000** in length.
The questions and answers were organized into a **one-line** format. A sample format is shown below:
```json
{
"question": "```\nFILE* file = fopen(some file)\n\npcap_t* pd = pcap_fopen_offline(file)\n\npcap_close(pd)\n\nfclose(file)\n```\n\nThis code occurs double free error.\n\nCould you explain about this happening?\n\nMy Guess is that pd and file pointers are sharing some datas.\n",
"answer": "As the documentation says, thepcap_closefunction closes the files associated with thepcap_tstructure passed to it. Closing the file again withfcloseis an error.\n"
}
``` |
yagnikposhiya/CommonVoiceCorpusTamil15 | 2023-10-03T06:19:50.000Z | [
"language:ta",
"license:apache-2.0",
"region:us"
] | yagnikposhiya | null | null | null | 0 | 0 | ---
license: apache-2.0
language:
- ta
--- |
lizhuang144/stack-exchange-preferences-20230914 | 2023-10-03T07:33:17.000Z | [
"license:apache-2.0",
"region:us"
] | lizhuang144 | null | null | null | 3 | 0 | ---
license: apache-2.0
dataset_info:
features:
- name: qid
dtype: int64
- name: question
dtype: string
- name: answers
list:
- name: answer_id
dtype: int64
- name: author
dtype: string
- name: author_id
dtype: int64
- name: author_profile
dtype: string
- name: pm_score
dtype: int64
- name: selected
dtype: bool
- name: text
dtype: string
- name: date
dtype: string
- name: metadata
sequence: string
splits:
- name: train
num_bytes: 48035017387
num_examples: 11033174
download_size: 12294290899
dataset_size: 48035017387
---
|
Hedinovianto/mashed | 2023-10-02T11:35:35.000Z | [
"region:us"
] | Hedinovianto | null | null | null | 0 | 0 | Entry not found |
hf-vision/course-assets | 2023-10-02T11:37:51.000Z | [
"license:apache-2.0",
"region:us"
] | hf-vision | null | null | null | 2 | 0 | ---
license: apache-2.0
---
|
dasom60620/cc_news | 2023-10-02T11:49:27.000Z | [
"region:us"
] | dasom60620 | null | null | null | 0 | 0 | Entry not found |
lunaluan/chatbox4_history | 2023-10-11T01:22:09.000Z | [
"region:us"
] | lunaluan | null | null | null | 0 | 0 | Entry not found |
vsarathy/DIARC-embodied-nlu-styled-4k-with-context | 2023-10-02T12:08:43.000Z | [
"region:us"
] | vsarathy | null | null | null | 0 | 0 | Entry not found |
BangumiBase/flipflappers | 2023-10-02T13:29:12.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Flip Flappers
This is the image base of bangumi Flip Flappers, we detected 26 characters, 1442 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 423 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 62 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 31 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 37 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 8 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 64 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 41 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 269 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 8 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 21 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 21 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 56 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 35 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 6 | [Download](14/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 15 | 32 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 15 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 9 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 17 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 25 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 18 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 40 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 16 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 6 | [Download](23/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 24 | 7 | [Download](24/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 152 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Sharka/CIVQA_easyocr_simple_valid | 2023-10-02T12:52:50.000Z | [
"region:us"
] | Sharka | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: id
dtype: string
- name: words
sequence: string
- name: answers
dtype: string
- name: bboxes
sequence:
sequence: float64
- name: answers_bboxes
sequence:
sequence: float64
- name: questions
dtype: string
- name: image
dtype: string
splits:
- name: validation
num_bytes: 48446674074
num_examples: 34159
download_size: 10985782991
dataset_size: 48446674074
---
# Dataset Card for "CIVQA_easyocr_simple_valid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BangumiBase/isitwrongtotrytopickupgirlsinadungeon | 2023-10-02T15:25:11.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Is It Wrong To Try To Pick Up Girls In A Dungeon?
This is the image base of bangumi Is It Wrong to Try to Pick Up Girls in a Dungeon?, we detected 79 characters, 5929 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 128 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 62 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 406 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 34 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 19 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 31 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 30 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 57 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 18 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 12 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 52 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 183 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 21 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 112 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 103 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 55 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 10 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 577 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 85 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 41 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 32 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 55 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 16 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 1150 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 36 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 22 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 20 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 25 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 17 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 6 | [Download](29/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 30 | 58 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 8 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 12 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 19 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 214 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 116 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 33 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 16 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 41 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 9 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 140 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 45 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 14 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 40 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 81 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 43 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 19 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 18 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 19 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 82 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 22 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 14 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 6 | [Download](52/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 53 | 17 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 14 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 79 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 133 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 13 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 13 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 195 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 97 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 27 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 13 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 62 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 8 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 9 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 8 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 33 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 6 | [Download](68/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 69 | 31 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 9 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 13 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 7 | [Download](72/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 73 | 22 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 6 | [Download](74/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 75 | 61 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 13 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 24 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 532 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/koisuruasteroid | 2023-10-02T13:43:32.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Koisuru Asteroid
This is the image base of bangumi Koisuru Asteroid, we detected 31 characters, 2450 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 501 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 15 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 22 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 40 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 222 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 45 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 14 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 94 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 425 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 14 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 18 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 26 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 17 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 114 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 27 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 15 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 13 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 39 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 245 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 27 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 39 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 187 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 9 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 15 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 12 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 33 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 12 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 71 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 6 | [Download](28/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 29 | 5 | [Download](29/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 128 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
kanoyo/kanoyo | 2023-10-06T09:33:17.000Z | [
"license:mit",
"region:us"
] | kanoyo | null | null | null | 0 | 0 | ---
license: mit
---
|
ocg2347/toy | 2023-10-09T10:58:16.000Z | [
"license:apache-2.0",
"region:us"
] | ocg2347 | null | null | null | 0 | 0 | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int32
- name: images
dtype: image
splits:
- name: train
num_bytes: 308311.0
num_examples: 3
download_size: 104574
dataset_size: 308311.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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