datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
lowem1/cc_news_ocr | ---
dataset_info:
features:
- name: tag
dtype: string
- name: ocr_data
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 19826223
num_examples: 2000
download_size: 7547846
dataset_size: 19826223
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cc_news_ocr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-exp2-0.1 | ---
pretty_name: Evaluation run of Mihaiii/Pallas-0.5-LASER-exp2-0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Mihaiii/Pallas-0.5-LASER-exp2-0.1](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-exp2-0.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-exp2-0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-05T05:05:37.988111](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-exp2-0.1/blob/main/results_2024-01-05T05-05-37.988111.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\": 0.7397821669932966,\n\
\ \"acc_stderr\": 0.029029829983116567,\n \"acc_norm\": 0.7458354364825251,\n\
\ \"acc_norm_stderr\": 0.029571133932098627,\n \"mc1\": 0.3990208078335373,\n\
\ \"mc1_stderr\": 0.017142825728496763,\n \"mc2\": 0.5524090883936386,\n\
\ \"mc2_stderr\": 0.015960461686079227\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6168941979522184,\n \"acc_stderr\": 0.014206472661672877,\n\
\ \"acc_norm\": 0.6296928327645052,\n \"acc_norm_stderr\": 0.01411129875167495\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6306512646883091,\n\
\ \"acc_stderr\": 0.004816421208654088,\n \"acc_norm\": 0.8210515833499303,\n\
\ \"acc_norm_stderr\": 0.003825257435209243\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8486842105263158,\n \"acc_stderr\": 0.029162631596843996,\n\
\ \"acc_norm\": 0.8486842105263158,\n \"acc_norm_stderr\": 0.029162631596843996\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\
\ \"acc_stderr\": 0.042295258468165044,\n \"acc_norm\": 0.77,\n \
\ \"acc_norm_stderr\": 0.042295258468165044\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372277,\n\
\ \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372277\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8611111111111112,\n\
\ \"acc_stderr\": 0.028919802956134902,\n \"acc_norm\": 0.8611111111111112,\n\
\ \"acc_norm_stderr\": 0.028919802956134902\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\
: 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7283236994219653,\n\
\ \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.7283236994219653,\n\
\ \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04975185951049946,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04975185951049946\n },\n\
\ \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n\
\ \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7617021276595745,\n \"acc_stderr\": 0.027851252973889778,\n\
\ \"acc_norm\": 0.7617021276595745,\n \"acc_norm_stderr\": 0.027851252973889778\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6140350877192983,\n\
\ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.6140350877192983,\n\
\ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7310344827586207,\n \"acc_stderr\": 0.036951833116502325,\n\
\ \"acc_norm\": 0.7310344827586207,\n \"acc_norm_stderr\": 0.036951833116502325\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.656084656084656,\n \"acc_stderr\": 0.024464426625596433,\n \"\
acc_norm\": 0.656084656084656,\n \"acc_norm_stderr\": 0.024464426625596433\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5476190476190477,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.5476190476190477,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"\
acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.6650246305418719,\n \"acc_stderr\": 0.033208527423483104,\n \"\
acc_norm\": 0.6650246305418719,\n \"acc_norm_stderr\": 0.033208527423483104\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\"\
: 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8363636363636363,\n \"acc_stderr\": 0.028887872395487946,\n\
\ \"acc_norm\": 0.8363636363636363,\n \"acc_norm_stderr\": 0.028887872395487946\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9090909090909091,\n \"acc_stderr\": 0.020482086775424225,\n \"\
acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.020482086775424225\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9792746113989638,\n \"acc_stderr\": 0.01028141701190904,\n\
\ \"acc_norm\": 0.9792746113989638,\n \"acc_norm_stderr\": 0.01028141701190904\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7974358974358975,\n \"acc_stderr\": 0.02037766097037139,\n \
\ \"acc_norm\": 0.7974358974358975,\n \"acc_norm_stderr\": 0.02037766097037139\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.44074074074074077,\n \"acc_stderr\": 0.030270671157284074,\n \
\ \"acc_norm\": 0.44074074074074077,\n \"acc_norm_stderr\": 0.030270671157284074\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8277310924369747,\n \"acc_stderr\": 0.024528664971305424,\n\
\ \"acc_norm\": 0.8277310924369747,\n \"acc_norm_stderr\": 0.024528664971305424\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.47019867549668876,\n \"acc_stderr\": 0.040752249922169775,\n \"\
acc_norm\": 0.47019867549668876,\n \"acc_norm_stderr\": 0.040752249922169775\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.908256880733945,\n \"acc_stderr\": 0.012376323409137092,\n \"\
acc_norm\": 0.908256880733945,\n \"acc_norm_stderr\": 0.012376323409137092\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6203703703703703,\n \"acc_stderr\": 0.03309682581119035,\n \"\
acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.03309682581119035\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9264705882352942,\n \"acc_stderr\": 0.01831885585008968,\n \"\
acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.01831885585008968\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065522,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065522\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\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744631,\n\
\ \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744631\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8842975206611571,\n \"acc_stderr\": 0.02919980245562281,\n \"\
acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.02919980245562281\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8518518518518519,\n\
\ \"acc_stderr\": 0.03434300243631,\n \"acc_norm\": 0.8518518518518519,\n\
\ \"acc_norm_stderr\": 0.03434300243631\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.026321383198783674,\n\
\ \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.026321383198783674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\
\ \"acc_stderr\": 0.01653462768431136,\n \"acc_norm\": 0.9316239316239316,\n\
\ \"acc_norm_stderr\": 0.01653462768431136\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8978288633461047,\n\
\ \"acc_stderr\": 0.010830724713134182,\n \"acc_norm\": 0.8978288633461047,\n\
\ \"acc_norm_stderr\": 0.010830724713134182\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8063583815028902,\n \"acc_stderr\": 0.021274230317515557,\n\
\ \"acc_norm\": 0.8063583815028902,\n \"acc_norm_stderr\": 0.021274230317515557\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6424581005586593,\n\
\ \"acc_stderr\": 0.016029394474894886,\n \"acc_norm\": 0.6424581005586593,\n\
\ \"acc_norm_stderr\": 0.016029394474894886\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7908496732026143,\n \"acc_stderr\": 0.023287685312334806,\n\
\ \"acc_norm\": 0.7908496732026143,\n \"acc_norm_stderr\": 0.023287685312334806\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7845659163987139,\n\
\ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\
\ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.018877353839571853,\n\
\ \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.018877353839571853\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6028368794326241,\n \"acc_stderr\": 0.0291898056735871,\n \
\ \"acc_norm\": 0.6028368794326241,\n \"acc_norm_stderr\": 0.0291898056735871\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5730117340286832,\n\
\ \"acc_stderr\": 0.012633353557534416,\n \"acc_norm\": 0.5730117340286832,\n\
\ \"acc_norm_stderr\": 0.012633353557534416\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.025187786660227248,\n\
\ \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.025187786660227248\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7973856209150327,\n \"acc_stderr\": 0.016261055283746138,\n \
\ \"acc_norm\": 0.7973856209150327,\n \"acc_norm_stderr\": 0.016261055283746138\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8367346938775511,\n \"acc_stderr\": 0.02366169917709861,\n\
\ \"acc_norm\": 0.8367346938775511,\n \"acc_norm_stderr\": 0.02366169917709861\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.900497512437811,\n\
\ \"acc_stderr\": 0.021166216304659386,\n \"acc_norm\": 0.900497512437811,\n\
\ \"acc_norm_stderr\": 0.021166216304659386\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.025172984350155764,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.025172984350155764\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3990208078335373,\n\
\ \"mc1_stderr\": 0.017142825728496763,\n \"mc2\": 0.5524090883936386,\n\
\ \"mc2_stderr\": 0.015960461686079227\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.797947908445146,\n \"acc_stderr\": 0.011285013754047443\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5276724791508719,\n \
\ \"acc_stderr\": 0.013751375538801323\n }\n}\n```"
repo_url: https://huggingface.co/Mihaiii/Pallas-0.5-LASER-exp2-0.1
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: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|arc:challenge|25_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|gsm8k|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hellaswag|10_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T05-05-37.988111.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T05-05-37.988111.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- '**/details_harness|winogrande|5_2024-01-05T05-05-37.988111.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-05T05-05-37.988111.parquet'
- config_name: results
data_files:
- split: 2024_01_05T05_05_37.988111
path:
- results_2024-01-05T05-05-37.988111.parquet
- split: latest
path:
- results_2024-01-05T05-05-37.988111.parquet
---
# Dataset Card for Evaluation run of Mihaiii/Pallas-0.5-LASER-exp2-0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Mihaiii/Pallas-0.5-LASER-exp2-0.1](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-exp2-0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-exp2-0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-05T05:05:37.988111](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-exp2-0.1/blob/main/results_2024-01-05T05-05-37.988111.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": 0.7397821669932966,
"acc_stderr": 0.029029829983116567,
"acc_norm": 0.7458354364825251,
"acc_norm_stderr": 0.029571133932098627,
"mc1": 0.3990208078335373,
"mc1_stderr": 0.017142825728496763,
"mc2": 0.5524090883936386,
"mc2_stderr": 0.015960461686079227
},
"harness|arc:challenge|25": {
"acc": 0.6168941979522184,
"acc_stderr": 0.014206472661672877,
"acc_norm": 0.6296928327645052,
"acc_norm_stderr": 0.01411129875167495
},
"harness|hellaswag|10": {
"acc": 0.6306512646883091,
"acc_stderr": 0.004816421208654088,
"acc_norm": 0.8210515833499303,
"acc_norm_stderr": 0.003825257435209243
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.03944624162501116,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8486842105263158,
"acc_stderr": 0.029162631596843996,
"acc_norm": 0.8486842105263158,
"acc_norm_stderr": 0.029162631596843996
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165044,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165044
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8075471698113208,
"acc_stderr": 0.024262979839372277,
"acc_norm": 0.8075471698113208,
"acc_norm_stderr": 0.024262979839372277
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8611111111111112,
"acc_stderr": 0.028919802956134902,
"acc_norm": 0.8611111111111112,
"acc_norm_stderr": 0.028919802956134902
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7283236994219653,
"acc_stderr": 0.03391750322321659,
"acc_norm": 0.7283236994219653,
"acc_norm_stderr": 0.03391750322321659
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5,
"acc_stderr": 0.04975185951049946,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04975185951049946
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7617021276595745,
"acc_stderr": 0.027851252973889778,
"acc_norm": 0.7617021276595745,
"acc_norm_stderr": 0.027851252973889778
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.6140350877192983,
"acc_stderr": 0.04579639422070434,
"acc_norm": 0.6140350877192983,
"acc_norm_stderr": 0.04579639422070434
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7310344827586207,
"acc_stderr": 0.036951833116502325,
"acc_norm": 0.7310344827586207,
"acc_norm_stderr": 0.036951833116502325
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.656084656084656,
"acc_stderr": 0.024464426625596433,
"acc_norm": 0.656084656084656,
"acc_norm_stderr": 0.024464426625596433
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5476190476190477,
"acc_stderr": 0.044518079590553275,
"acc_norm": 0.5476190476190477,
"acc_norm_stderr": 0.044518079590553275
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9032258064516129,
"acc_stderr": 0.016818943416345197,
"acc_norm": 0.9032258064516129,
"acc_norm_stderr": 0.016818943416345197
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6650246305418719,
"acc_stderr": 0.033208527423483104,
"acc_norm": 0.6650246305418719,
"acc_norm_stderr": 0.033208527423483104
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8363636363636363,
"acc_stderr": 0.028887872395487946,
"acc_norm": 0.8363636363636363,
"acc_norm_stderr": 0.028887872395487946
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9090909090909091,
"acc_stderr": 0.020482086775424225,
"acc_norm": 0.9090909090909091,
"acc_norm_stderr": 0.020482086775424225
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9792746113989638,
"acc_stderr": 0.01028141701190904,
"acc_norm": 0.9792746113989638,
"acc_norm_stderr": 0.01028141701190904
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7974358974358975,
"acc_stderr": 0.02037766097037139,
"acc_norm": 0.7974358974358975,
"acc_norm_stderr": 0.02037766097037139
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.44074074074074077,
"acc_stderr": 0.030270671157284074,
"acc_norm": 0.44074074074074077,
"acc_norm_stderr": 0.030270671157284074
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8277310924369747,
"acc_stderr": 0.024528664971305424,
"acc_norm": 0.8277310924369747,
"acc_norm_stderr": 0.024528664971305424
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.47019867549668876,
"acc_stderr": 0.040752249922169775,
"acc_norm": 0.47019867549668876,
"acc_norm_stderr": 0.040752249922169775
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.908256880733945,
"acc_stderr": 0.012376323409137092,
"acc_norm": 0.908256880733945,
"acc_norm_stderr": 0.012376323409137092
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6203703703703703,
"acc_stderr": 0.03309682581119035,
"acc_norm": 0.6203703703703703,
"acc_norm_stderr": 0.03309682581119035
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9264705882352942,
"acc_stderr": 0.01831885585008968,
"acc_norm": 0.9264705882352942,
"acc_norm_stderr": 0.01831885585008968
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8987341772151899,
"acc_stderr": 0.019637720526065522,
"acc_norm": 0.8987341772151899,
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"acc": 0.8518518518518519,
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"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm_stderr": 0.025172984350155764
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3990208078335373,
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"mc2": 0.5524090883936386,
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},
"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.5276724791508719,
"acc_stderr": 0.013751375538801323
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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[More Information Needed] |
jacobbieker/era5-42hour-test | ---
license: mit
---
|
whitefox44/ImprovementFromReflection | ---
license: apache-2.0
---
|
bh8648/split_dataset_16-1 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: page_num
dtype: int64
splits:
- name: train
num_bytes: 417525
num_examples: 105
download_size: 211741
dataset_size: 417525
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "split_dataset_16-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BeIR/scifact-qrels | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
jyang/webshop_inst_goal_pairs_truth | ---
license: mit
---
|
Ankursingh/openwebtext_10K | ---
license: mpl-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 49626451.28403377
num_examples: 10000
- name: val
num_bytes: 19885319.02951233
num_examples: 4007
download_size: 41616125
dataset_size: 69511770.31354609
---
|
EgilKarlsen/Spirit_GPTNEO_Finetuned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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- name: '2036'
dtype: float32
- name: '2037'
dtype: float32
- name: '2038'
dtype: float32
- name: '2039'
dtype: float32
- name: '2040'
dtype: float32
- name: '2041'
dtype: float32
- name: '2042'
dtype: float32
- name: '2043'
dtype: float32
- name: '2044'
dtype: float32
- name: '2045'
dtype: float32
- name: '2046'
dtype: float32
- name: '2047'
dtype: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 307650065.625
num_examples: 37500
- name: test
num_bytes: 102550020.0
num_examples: 12500
download_size: 565195315
dataset_size: 410200085.625
---
# Dataset Card for "Spirit_GPTNEO_Finetuned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Felladrin/ChatML-open-instruct | ---
license: cc-by-3.0
task_categories:
- text-generation
- text2text-generation
language:
- en
size_categories:
- 100K<n<1M
---
[VMware/open-instruct](https://huggingface.co/datasets/VMware/open-instruct) in ChatML format, ready to use in [HuggingFace TRL's SFT Trainer](https://huggingface.co/docs/trl/main/en/sft_trainer).
Python code used for conversion:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("VMware/open-instruct", split="train")
def format(columns):
messages = [
{
"role": "user",
"content": columns["instruction"].strip(),
},
{
"role": "assistant",
"content": columns["response"].strip(),
},
]
return { "text": tokenizer.apply_chat_template(messages, tokenize=False) }
dataset.map(format).select_columns(['text', 'source', 'task_name']).to_parquet("train.parquet")
``` |
AlekseyKorshuk/product-photography-vivid-v0 | ---
dataset_info:
features:
- name: image
dtype: image
- name: id
dtype: string
- name: image_mask
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 9232448247.37657
num_examples: 5093
download_size: 9418890722
dataset_size: 9232448247.37657
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Artificio/WikiArt_mini_demos | ---
dataset_info:
features:
- name: title
dtype: string
- name: artist
dtype: string
- name: date
dtype: string
- name: genre
dtype: string
- name: style
dtype: string
- name: description
dtype: string
- name: filename
dtype: string
- name: image
dtype: image
- name: resnet50_non_robust_features_2048
sequence: float32
- name: resnet50_robust_features_2048
sequence: float32
- name: resnet50_robust_feats
sequence: float32
- name: resnet50_non_robust_feats
sequence: float32
splits:
- name: train
num_bytes: 467403497.0
num_examples: 10000
download_size: 445315266
dataset_size: 467403497.0
---
# Dataset Card for "WikiArt_mini_demos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
togakure/Eden | ---
license: openrail
---
|
open-llm-leaderboard/details_timdettmers__guanaco-33b-merged | ---
pretty_name: Evaluation run of timdettmers/guanaco-33b-merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [timdettmers/guanaco-33b-merged](https://huggingface.co/timdettmers/guanaco-33b-merged)\
\ 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_timdettmers__guanaco-33b-merged\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-07-20T14:01:44.026263](https://huggingface.co/datasets/open-llm-leaderboard/details_timdettmers__guanaco-33b-merged/blob/main/results_2023-07-20T14%3A01%3A44.026263.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\": 0.5404553018205109,\n\
\ \"acc_stderr\": 0.03488622237927161,\n \"acc_norm\": 0.5444824613318672,\n\
\ \"acc_norm_stderr\": 0.03486249375448495,\n \"mc1\": 0.34761321909424725,\n\
\ \"mc1_stderr\": 0.016670769188897306,\n \"mc2\": 0.5121992740888713,\n\
\ \"mc2_stderr\": 0.014650490351006002\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5870307167235495,\n \"acc_stderr\": 0.014388344935398326,\n\
\ \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.014150631435111726\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6446922923720374,\n\
\ \"acc_stderr\": 0.004776283203468098,\n \"acc_norm\": 0.8447520414260108,\n\
\ \"acc_norm_stderr\": 0.003614007841341989\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.04033565667848319,\n\
\ \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.04033565667848319\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.5622641509433962,\n \"acc_stderr\": 0.030533338430467516,\n\
\ \"acc_norm\": 0.5622641509433962,\n \"acc_norm_stderr\": 0.030533338430467516\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5625,\n\
\ \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.5625,\n \
\ \"acc_norm_stderr\": 0.04148415739394154\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\
: 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5144508670520231,\n\
\ \"acc_stderr\": 0.03810871630454764,\n \"acc_norm\": 0.5144508670520231,\n\
\ \"acc_norm_stderr\": 0.03810871630454764\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\":\
\ 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n\
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099835\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n\
\ \"acc_stderr\": 0.04537815354939391,\n \"acc_norm\": 0.3684210526315789,\n\
\ \"acc_norm_stderr\": 0.04537815354939391\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.31216931216931215,\n \"acc_stderr\": 0.0238652068369726,\n \"\
acc_norm\": 0.31216931216931215,\n \"acc_norm_stderr\": 0.0238652068369726\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\
\ \"acc_stderr\": 0.04073524322147126,\n \"acc_norm\": 0.29365079365079366,\n\
\ \"acc_norm_stderr\": 0.04073524322147126\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.6290322580645161,\n \"acc_stderr\": 0.027480541887953593,\n \"\
acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.027480541887953593\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3793103448275862,\n \"acc_stderr\": 0.03413963805906235,\n \"\
acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.03413963805906235\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.035014387062967806,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.035014387062967806\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7222222222222222,\n \"acc_stderr\": 0.03191178226713547,\n \"\
acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.03191178226713547\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7150259067357513,\n \"acc_stderr\": 0.032577140777096614,\n\
\ \"acc_norm\": 0.7150259067357513,\n \"acc_norm_stderr\": 0.032577140777096614\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.49230769230769234,\n \"acc_stderr\": 0.025348006031534778,\n\
\ \"acc_norm\": 0.49230769230769234,\n \"acc_norm_stderr\": 0.025348006031534778\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02730914058823019,\n \
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02730914058823019\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.03242225027115006,\n \
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03242225027115006\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7302752293577982,\n \"acc_stderr\": 0.019028486711115438,\n \"\
acc_norm\": 0.7302752293577982,\n \"acc_norm_stderr\": 0.019028486711115438\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.375,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.375,\n\
\ \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967409,\n\
\ \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967409\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \
\ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5874439461883408,\n\
\ \"acc_stderr\": 0.03304062175449297,\n \"acc_norm\": 0.5874439461883408,\n\
\ \"acc_norm_stderr\": 0.03304062175449297\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6412213740458015,\n \"acc_stderr\": 0.04206739313864908,\n\
\ \"acc_norm\": 0.6412213740458015,\n \"acc_norm_stderr\": 0.04206739313864908\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7272727272727273,\n \"acc_stderr\": 0.04065578140908705,\n \"\
acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04065578140908705\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6111111111111112,\n\
\ \"acc_stderr\": 0.04712821257426769,\n \"acc_norm\": 0.6111111111111112,\n\
\ \"acc_norm_stderr\": 0.04712821257426769\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6380368098159509,\n \"acc_stderr\": 0.037757007291414416,\n\
\ \"acc_norm\": 0.6380368098159509,\n \"acc_norm_stderr\": 0.037757007291414416\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\
\ \"acc_stderr\": 0.04616143075028546,\n \"acc_norm\": 0.38392857142857145,\n\
\ \"acc_norm_stderr\": 0.04616143075028546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6310679611650486,\n \"acc_stderr\": 0.0477761518115674,\n\
\ \"acc_norm\": 0.6310679611650486,\n \"acc_norm_stderr\": 0.0477761518115674\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7863247863247863,\n\
\ \"acc_stderr\": 0.026853450377009154,\n \"acc_norm\": 0.7863247863247863,\n\
\ \"acc_norm_stderr\": 0.026853450377009154\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6756066411238825,\n\
\ \"acc_stderr\": 0.0167409290471627,\n \"acc_norm\": 0.6756066411238825,\n\
\ \"acc_norm_stderr\": 0.0167409290471627\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5491329479768786,\n \"acc_stderr\": 0.026788811931562757,\n\
\ \"acc_norm\": 0.5491329479768786,\n \"acc_norm_stderr\": 0.026788811931562757\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2569832402234637,\n\
\ \"acc_stderr\": 0.01461446582196632,\n \"acc_norm\": 0.2569832402234637,\n\
\ \"acc_norm_stderr\": 0.01461446582196632\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5718954248366013,\n \"acc_stderr\": 0.028332397483664278,\n\
\ \"acc_norm\": 0.5718954248366013,\n \"acc_norm_stderr\": 0.028332397483664278\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6012861736334405,\n\
\ \"acc_stderr\": 0.027809322585774496,\n \"acc_norm\": 0.6012861736334405,\n\
\ \"acc_norm_stderr\": 0.027809322585774496\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6172839506172839,\n \"acc_stderr\": 0.027044538138402595,\n\
\ \"acc_norm\": 0.6172839506172839,\n \"acc_norm_stderr\": 0.027044538138402595\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.42907801418439717,\n \"acc_stderr\": 0.029525914302558555,\n \
\ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.029525914302558555\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.424380704041721,\n\
\ \"acc_stderr\": 0.01262334375743002,\n \"acc_norm\": 0.424380704041721,\n\
\ \"acc_norm_stderr\": 0.01262334375743002\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.02997280717046462,\n\
\ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.02997280717046462\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5212418300653595,\n \"acc_stderr\": 0.020209572388600248,\n \
\ \"acc_norm\": 0.5212418300653595,\n \"acc_norm_stderr\": 0.020209572388600248\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5918367346938775,\n \"acc_stderr\": 0.03146465712827424,\n\
\ \"acc_norm\": 0.5918367346938775,\n \"acc_norm_stderr\": 0.03146465712827424\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6915422885572139,\n\
\ \"acc_stderr\": 0.032658195885126966,\n \"acc_norm\": 0.6915422885572139,\n\
\ \"acc_norm_stderr\": 0.032658195885126966\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \
\ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322626\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7251461988304093,\n \"acc_stderr\": 0.03424042924691584,\n\
\ \"acc_norm\": 0.7251461988304093,\n \"acc_norm_stderr\": 0.03424042924691584\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.34761321909424725,\n\
\ \"mc1_stderr\": 0.016670769188897306,\n \"mc2\": 0.5121992740888713,\n\
\ \"mc2_stderr\": 0.014650490351006002\n }\n}\n```"
repo_url: https://huggingface.co/timdettmers/guanaco-33b-merged
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|arc:challenge|25_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hellaswag|10_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T14:01:44.026263.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T14:01:44.026263.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-20T14:01:44.026263.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-20T14:01:44.026263.parquet'
- config_name: results
data_files:
- split: 2023_07_20T14_01_44.026263
path:
- results_2023-07-20T14:01:44.026263.parquet
- split: latest
path:
- results_2023-07-20T14:01:44.026263.parquet
---
# Dataset Card for Evaluation run of timdettmers/guanaco-33b-merged
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/timdettmers/guanaco-33b-merged
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [timdettmers/guanaco-33b-merged](https://huggingface.co/timdettmers/guanaco-33b-merged) 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_timdettmers__guanaco-33b-merged",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-07-20T14:01:44.026263](https://huggingface.co/datasets/open-llm-leaderboard/details_timdettmers__guanaco-33b-merged/blob/main/results_2023-07-20T14%3A01%3A44.026263.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": 0.5404553018205109,
"acc_stderr": 0.03488622237927161,
"acc_norm": 0.5444824613318672,
"acc_norm_stderr": 0.03486249375448495,
"mc1": 0.34761321909424725,
"mc1_stderr": 0.016670769188897306,
"mc2": 0.5121992740888713,
"mc2_stderr": 0.014650490351006002
},
"harness|arc:challenge|25": {
"acc": 0.5870307167235495,
"acc_stderr": 0.014388344935398326,
"acc_norm": 0.6245733788395904,
"acc_norm_stderr": 0.014150631435111726
},
"harness|hellaswag|10": {
"acc": 0.6446922923720374,
"acc_stderr": 0.004776283203468098,
"acc_norm": 0.8447520414260108,
"acc_norm_stderr": 0.003614007841341989
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.48148148148148145,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5657894736842105,
"acc_stderr": 0.04033565667848319,
"acc_norm": 0.5657894736842105,
"acc_norm_stderr": 0.04033565667848319
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5622641509433962,
"acc_stderr": 0.030533338430467516,
"acc_norm": 0.5622641509433962,
"acc_norm_stderr": 0.030533338430467516
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5625,
"acc_stderr": 0.04148415739394154,
"acc_norm": 0.5625,
"acc_norm_stderr": 0.04148415739394154
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5144508670520231,
"acc_stderr": 0.03810871630454764,
"acc_norm": 0.5144508670520231,
"acc_norm_stderr": 0.03810871630454764
},
"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": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4595744680851064,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.4595744680851064,
"acc_norm_stderr": 0.03257901482099835
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3684210526315789,
"acc_stderr": 0.04537815354939391,
"acc_norm": 0.3684210526315789,
"acc_norm_stderr": 0.04537815354939391
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4413793103448276,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.4413793103448276,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.31216931216931215,
"acc_stderr": 0.0238652068369726,
"acc_norm": 0.31216931216931215,
"acc_norm_stderr": 0.0238652068369726
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.29365079365079366,
"acc_stderr": 0.04073524322147126,
"acc_norm": 0.29365079365079366,
"acc_norm_stderr": 0.04073524322147126
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6290322580645161,
"acc_stderr": 0.027480541887953593,
"acc_norm": 0.6290322580645161,
"acc_norm_stderr": 0.027480541887953593
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3793103448275862,
"acc_stderr": 0.03413963805906235,
"acc_norm": 0.3793103448275862,
"acc_norm_stderr": 0.03413963805906235
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7212121212121212,
"acc_stderr": 0.035014387062967806,
"acc_norm": 0.7212121212121212,
"acc_norm_stderr": 0.035014387062967806
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.03191178226713547,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.03191178226713547
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7150259067357513,
"acc_stderr": 0.032577140777096614,
"acc_norm": 0.7150259067357513,
"acc_norm_stderr": 0.032577140777096614
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.49230769230769234,
"acc_stderr": 0.025348006031534778,
"acc_norm": 0.49230769230769234,
"acc_norm_stderr": 0.025348006031534778
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2777777777777778,
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},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.5808823529411765,
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.04607582090719976
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5918367346938775,
"acc_stderr": 0.03146465712827424,
"acc_norm": 0.5918367346938775,
"acc_norm_stderr": 0.03146465712827424
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6915422885572139,
"acc_stderr": 0.032658195885126966,
"acc_norm": 0.6915422885572139,
"acc_norm_stderr": 0.032658195885126966
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.78,
"acc_stderr": 0.041633319989322626,
"acc_norm": 0.78,
"acc_norm_stderr": 0.041633319989322626
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4578313253012048,
"acc_stderr": 0.038786267710023595,
"acc_norm": 0.4578313253012048,
"acc_norm_stderr": 0.038786267710023595
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7251461988304093,
"acc_stderr": 0.03424042924691584,
"acc_norm": 0.7251461988304093,
"acc_norm_stderr": 0.03424042924691584
},
"harness|truthfulqa:mc|0": {
"mc1": 0.34761321909424725,
"mc1_stderr": 0.016670769188897306,
"mc2": 0.5121992740888713,
"mc2_stderr": 0.014650490351006002
}
}
```
### 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] |
AzhrSlm/training-dataset-demo | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4201526
num_examples: 1000
download_size: 2247083
dataset_size: 4201526
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ml20max/nature-outdoor | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 6949160.0
num_examples: 16
download_size: 0
dataset_size: 6949160.0
---
# Dataset Card for "nature-outdoor"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/ads-fashion | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 2666953
num_examples: 10000
download_size: 272530
dataset_size: 2666953
---
# Dataset Card for "ads-fashion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/lotte_pooled_test_forum | ---
pretty_name: '`lotte/pooled/test/forum`'
viewer: false
source_datasets: ['irds/lotte_pooled_test']
task_categories:
- text-retrieval
---
# Dataset Card for `lotte/pooled/test/forum`
The `lotte/pooled/test/forum` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/pooled/test/forum).
# Data
This dataset provides:
- `queries` (i.e., topics); count=10,025
- `qrels`: (relevance assessments); count=61,536
- For `docs`, use [`irds/lotte_pooled_test`](https://huggingface.co/datasets/irds/lotte_pooled_test)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_pooled_test_forum', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_pooled_test_forum', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
tyzhu/squad_qa_wrong_title_v5_full_recite_full_passage_random_permute_rerun_1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 5341144.491906475
num_examples: 2875
- name: validation
num_bytes: 599488
num_examples: 300
download_size: 1559487
dataset_size: 5940632.491906475
---
# Dataset Card for "squad_qa_wrong_title_v5_full_recite_full_passage_random_permute_rerun_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kaleemWaheed/twitter_dataset_1713045795 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 11714
num_examples: 26
download_size: 9584
dataset_size: 11714
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Saxo/ko_medical_meadow_med_qa_options_linkbricks_single_dataset_with_prompt_text_huggingface | ---
license: apache-2.0
---
|
kaleemWaheed/twitter_dataset_1713078651 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 11705
num_examples: 26
download_size: 9011
dataset_size: 11705
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
teilomillet/questions_data_analyst | ---
license: cc-by-4.0
---
|
cellophaneZR/AE_dataset | ---
license: cc-by-nc-4.0
---
|
Sofoklis/polyC | ---
dataset_info:
features:
- name: number
dtype: int64
- name: name
dtype: string
- name: sequence
dtype: string
- name: spaced_sequence
dtype: string
- name: array
sequence:
sequence: float64
- name: image
dtype: image
splits:
- name: train
num_bytes: 724952.7
num_examples: 90
- name: test
num_bytes: 80550.3
num_examples: 10
- name: validation
num_bytes: 144990.54
num_examples: 18
download_size: 17197
dataset_size: 950493.54
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
japanese-asr/whisper_transcriptions.reazonspeech.all_52 | ---
dataset_info:
config_name: all
features:
- name: name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 30276576128.0
num_examples: 267231
download_size: 30039961186
dataset_size: 30276576128.0
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
---
|
ameya-akkalkotkar/MarketMailDataset | ---
dataset_info:
features:
- name: product
dtype: string
- name: description
dtype: string
- name: marketing_email
dtype: string
splits:
- name: train
num_bytes: 123817
num_examples: 98
download_size: 72896
dataset_size: 123817
---
# Dataset Card for "MarketMailDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AlekseyKorshuk/CS1QACensoredClassEval-responses-starchat-annotations | ---
dataset_info:
features:
- name: model_input
list:
- name: content
dtype: string
- name: role
dtype: string
- name: baseline_response
dtype: string
- name: chat_response
dtype: string
- name: starchat_response
dtype: string
- name: starchat_annotation
dtype: float64
splits:
- name: train
num_bytes: 303777
num_examples: 100
download_size: 113708
dataset_size: 303777
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
one-sec-cv12/chunk_254 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 13943096064.0
num_examples: 145168
download_size: 12204547841
dataset_size: 13943096064.0
---
# Dataset Card for "chunk_254"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
feedback-to-code/retrieval_bench_1 | ---
license: mit
---
|
CyberHarem/erebus_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of erebus/エレバス/黑暗界 (Azur Lane)
This is the dataset of erebus/エレバス/黑暗界 (Azur Lane), containing 121 images and their tags.
The core tags of this character are `long_hair, red_eyes, bangs, bow, white_hair, red_bow, very_long_hair, hat, grey_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 121 | 138.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/erebus_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 121 | 86.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/erebus_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 295 | 182.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/erebus_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 121 | 124.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/erebus_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 295 | 240.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/erebus_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/erebus_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, black_skirt, looking_at_viewer, solo, white_shirt, black_cape, center_frills, closed_mouth, torn_cape, white_background, white_gloves, blush, hood_up, simple_background, suspender_skirt, holding, hooded_cape, puffy_short_sleeves, striped_thighhighs, high-waist_skirt, breasts, belt_boots, black_bow, black_footwear, multicolored_cape |
| 1 | 6 |  |  |  |  |  | 1girl, black_bow, black_dress, cross_necklace, long_sleeves, looking_at_viewer, solo, capelet, hair_between_eyes, white_background, blush, frills, fur_trim, garter_straps, gothic_lolita, heart, black_headwear, closed_mouth, holding_bag, simple_background, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_skirt | looking_at_viewer | solo | white_shirt | black_cape | center_frills | closed_mouth | torn_cape | white_background | white_gloves | blush | hood_up | simple_background | suspender_skirt | holding | hooded_cape | puffy_short_sleeves | striped_thighhighs | high-waist_skirt | breasts | belt_boots | black_bow | black_footwear | multicolored_cape | black_dress | cross_necklace | long_sleeves | capelet | hair_between_eyes | frills | fur_trim | garter_straps | gothic_lolita | heart | black_headwear | holding_bag | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:--------------------|:-------|:--------------|:-------------|:----------------|:---------------|:------------|:-------------------|:---------------|:--------|:----------|:--------------------|:------------------|:----------|:--------------|:----------------------|:---------------------|:-------------------|:----------|:-------------|:------------|:-----------------|:--------------------|:--------------|:-----------------|:---------------|:----------|:--------------------|:---------|:-----------|:----------------|:----------------|:--------|:-----------------|:--------------|:-------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | | | | X | | X | | X | | X | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
AIARTCHAN/lora-bmpmi | ---
license: creativeml-openrail-m
tags:
- lora
- aiartchan
- stable-diffusion
---
# Lora - 븝미
## Dataset Description
- **원본** [19) 븜미 로라](https://arca.live/b/aiart/71610355)
강도 0.6 ~ 0.7 사용
프롬프트 : bmpmi, red hair, red eyes, long hair (필요시 Twintails 추가)
[다운로드](https://huggingface.co/datasets/AIARTCHAN/lora-bmpmi/resolve/main/Bmpmi.safetensors)
|
haor/openniji_5_1-27 | ---
task_categories:
- text-to-image
language:
- en
- ja
- ko
tags:
- anime
- dataset
- Nijijourney
- Midjourney
- discord
size_categories:
- 100K<n<1M
license: cc-by-nc-4.0
---
# Dataset Card for haor/openniji_5_1-27
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Information](#dataset-information)
- [Dataset Statistics](#dataset-statistics)
- [Usage](#usage)
- [Changelog](#changelog)
<a name="dataset-description"></a>
## Dataset Description
The openniji_5_1-27 dataset is a subset of the OpenNiji-Dataset provided by ShoukanLabs. This subset, comprising of image-text pairs collected after May 1st. The images are compressed to 95% using the webp format, and the associated texts are included as prompts.
<a name="dataset-information"></a>
## Dataset Information
- **Format:**
- Image: WebP (95% compressed)
- Text: txt (containing prompt content)
- **Date of Collection:** Collected after May 1st
- **License:** Please check [ShoukanLabs/OpenNiji-Dataset](https://huggingface.co/datasets/ShoukanLabs/OpenNiji-Dataset) for more details
<a name="dataset-statistics"></a>
## Dataset Statistics
Information in this section depends on the actual number and details of the collected data.
- Total number of image-text pairs: 118261
- Image-text pairs with aesthetic score greater than 0.9: 51430
- Image-text pairs with aesthetic scores ranging from 0.8 to 0.9: 27989
- 
<a name="usage"></a>
## Usage
Source dataset: [ShoukanLabs/OpenNiji-Dataset](https://huggingface.co/datasets/ShoukanLabs/OpenNiji-Dataset)
This dataset: [haor/openniji_5_1-27](https://huggingface.co/datasets/haor/openniji_5_1-27)
For usage rights, permissions, citations and more, please refer to the original dataset repository: [ShoukanLabs/OpenNiji-Dataset](https://huggingface.co/datasets/ShoukanLabs/OpenNiji-Dataset)
<a name="changelog"></a>
## Changelog
- Version 1.0: Initial release, containing data collected from May 1st to May 27th.
This dataset card was last updated on: July 31, 2023
|
Talha185/tti-tester | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 17736676.0
num_examples: 15
download_size: 17713909
dataset_size: 17736676.0
---
# Dataset Card for "tti-tester"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nisaar/Testing_Law | ---
license: afl-3.0
---
|
CyberHarem/tsukioka_kogane_theidolmstershinycolors | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of tsukioka_kogane/月岡恋鐘 (THE iDOLM@STER: SHINY COLORS)
This is the dataset of tsukioka_kogane/月岡恋鐘 (THE iDOLM@STER: SHINY COLORS), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, breasts, bangs, large_breasts, brown_eyes, bow, ribbon, hair_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 823.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukioka_kogane_theidolmstershinycolors/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 433.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukioka_kogane_theidolmstershinycolors/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1261 | 968.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukioka_kogane_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 715.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukioka_kogane_theidolmstershinycolors/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1261 | 1.42 GiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukioka_kogane_theidolmstershinycolors/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tsukioka_kogane_theidolmstershinycolors',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, looking_at_viewer, navel, open_mouth, smile, solo, blush, simple_background, white_background, collarbone, side-tie_bikini_bottom, swept_bangs, thighs, white_bikini, covered_nipples, huge_breasts, on_side, see-through |
| 1 | 55 |  |  |  |  |  | 1girl, solo, blush, looking_at_viewer, ponytail, blue_bow, smile, blue_skirt, open_mouth, white_background, simple_background, tied_shirt, white_shirt, sleeveless_shirt, blue_ribbon, bracelet |
| 2 | 16 |  |  |  |  |  | 1girl, cleavage, solo, blush, collarbone, looking_at_viewer, navel, smile, outdoors, pink_bikini, day, open_mouth, bare_shoulders, blue_sky, flower, swept_bangs, head_wreath, ocean, side-tie_bikini_bottom, thighs, twin_braids, water, beach, cloud, see-through |
| 3 | 21 |  |  |  |  |  | 1girl, looking_at_viewer, solo, sun_hat, blush, cleavage, flower_necklace, smile, bare_shoulders, bracelet, plaid_bikini, swept_bangs, navel, floral_print, open_mouth, outdoors, collarbone, frills, ocean, side-tie_bikini_bottom, simple_background, sky, white_background |
| 4 | 6 |  |  |  |  |  | 1girl, blush, completely_nude, navel, nipples, ponytail, blue_bow, looking_at_viewer, simple_background, solo, white_background, collarbone, closed_mouth, mosaic_censoring, pussy, sweat, swept_bangs |
| 5 | 15 |  |  |  |  |  | 1girl, blush, cleavage, looking_at_viewer, solo, bra, navel, collarbone, thighs, underwear_only, swept_bangs, bare_shoulders, open_mouth, simple_background, smile, white_background, white_panties |
| 6 | 9 |  |  |  |  |  | 1girl, blush, choker, collarbone, looking_at_viewer, open_mouth, solo, blue_shirt, bow_hairband, hair_ribbon, cleavage, puffy_short_sleeves, white_skirt, :d, see-through |
| 7 | 8 |  |  |  |  |  | 1girl, black_gloves, skirt, solo, corset, looking_at_viewer, open_mouth, puffy_short_sleeves, thighhighs, blush, choker, holding_microphone, smile, black_dress, frills, gears, hair_ornament, pink_eyes, rose, white_background, belt, black_footwear, boots, garter_straps, hat, simple_background |
| 8 | 8 |  |  |  |  |  | 1girl, blush, long_sleeves, looking_at_viewer, solo, closed_mouth, smile, white_background, ribbed_sweater, simple_background, bare_shoulders, blue_sweater, shoulder_cutout, swept_bangs, turtleneck_sweater, black_skirt, hair_ribbon, upper_body |
| 9 | 21 |  |  |  |  |  | 1girl, blush, hetero, nipples, 1boy, solo_focus, navel, completely_nude, open_mouth, penis, pussy, looking_at_viewer, spread_legs, sweat, swept_bangs, sex, vaginal, mosaic_censoring, lying, pov, female_pubic_hair, heart |
| 10 | 10 |  |  |  |  |  | 1girl, blush, cleavage, rabbit_ears, solo, fake_animal_ears, navel, wrist_cuffs, hairband, looking_at_viewer, open_mouth, bare_shoulders, detached_collar, midriff, pink_eyes, pom_pom_(clothes), frilled_skirt, smile, white_skirt, red_bow, white_background |
| 11 | 13 |  |  |  |  |  | 1girl, playboy_bunny, cleavage, rabbit_ears, solo, blush, detached_collar, fake_animal_ears, looking_at_viewer, wrist_cuffs, bare_shoulders, black_leotard, bowtie, simple_background, smile, strapless_leotard, white_background, covered_navel, rabbit_tail, open_mouth, black_pantyhose, closed_mouth, highleg_leotard |
| 12 | 17 |  |  |  |  |  | 1girl, kimono, looking_at_viewer, smile, solo, floral_print, blush, wide_sleeves, obi, flower, hair_ornament, long_sleeves, open_mouth, black_gloves, hat, holding_umbrella, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | cleavage | looking_at_viewer | navel | open_mouth | smile | solo | blush | simple_background | white_background | collarbone | side-tie_bikini_bottom | swept_bangs | thighs | white_bikini | covered_nipples | huge_breasts | on_side | see-through | ponytail | blue_bow | blue_skirt | tied_shirt | white_shirt | sleeveless_shirt | blue_ribbon | bracelet | outdoors | pink_bikini | day | blue_sky | flower | head_wreath | ocean | twin_braids | water | beach | cloud | sun_hat | flower_necklace | plaid_bikini | floral_print | frills | sky | completely_nude | nipples | closed_mouth | mosaic_censoring | pussy | sweat | bra | underwear_only | white_panties | choker | blue_shirt | bow_hairband | hair_ribbon | puffy_short_sleeves | white_skirt | :d | black_gloves | skirt | corset | thighhighs | holding_microphone | black_dress | gears | hair_ornament | pink_eyes | rose | belt | black_footwear | boots | garter_straps | hat | long_sleeves | ribbed_sweater | blue_sweater | shoulder_cutout | turtleneck_sweater | black_skirt | upper_body | hetero | 1boy | solo_focus | penis | spread_legs | sex | vaginal | lying | pov | female_pubic_hair | heart | rabbit_ears | fake_animal_ears | wrist_cuffs | hairband | detached_collar | midriff | pom_pom_(clothes) | frilled_skirt | red_bow | playboy_bunny | black_leotard | bowtie | strapless_leotard | covered_navel | rabbit_tail | black_pantyhose | highleg_leotard | kimono | wide_sleeves | obi | holding_umbrella |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------------|:-----------|:--------------------|:--------|:-------------|:--------|:-------|:--------|:--------------------|:-------------------|:-------------|:-------------------------|:--------------|:---------|:---------------|:------------------|:---------------|:----------|:--------------|:-----------|:-----------|:-------------|:-------------|:--------------|:-------------------|:--------------|:-----------|:-----------|:--------------|:------|:-----------|:---------|:--------------|:--------|:--------------|:--------|:--------|:--------|:----------|:------------------|:---------------|:---------------|:---------|:------|:------------------|:----------|:---------------|:-------------------|:--------|:--------|:------|:-----------------|:----------------|:---------|:-------------|:---------------|:--------------|:----------------------|:--------------|:-----|:---------------|:--------|:---------|:-------------|:---------------------|:--------------|:--------|:----------------|:------------|:-------|:-------|:-----------------|:--------|:----------------|:------|:---------------|:-----------------|:---------------|:------------------|:---------------------|:--------------|:-------------|:---------|:-------|:-------------|:--------|:--------------|:------|:----------|:--------|:------|:--------------------|:--------|:--------------|:-------------------|:--------------|:-----------|:------------------|:----------|:--------------------|:----------------|:----------|:----------------|:----------------|:---------|:--------------------|:----------------|:--------------|:------------------|:------------------|:---------|:---------------|:------|:-------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 55 |  |  |  |  |  | X | | | X | | X | X | X | X | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | X | X | X | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | X | X | | | | | | X | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | | X | X | | | X | X | X | X | X | | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | | X | X | | X | | X | X | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 8 |  |  |  |  |  | X | X | | X | | | X | X | X | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 21 |  |  |  |  |  | X | | | X | X | X | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 10 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 11 | 13 |  |  |  |  |  | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | X | | | | | X | X | X | X | X | X | X | X | | | | |
| 12 | 17 |  |  |  |  |  | X | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X |
|
FaalSa/data6 | ---
dataset_info:
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: item_id
dtype: string
- name: feat_static_cat
sequence: uint64
splits:
- name: train
num_bytes: 17309
num_examples: 1
- name: validation
num_bytes: 17789
num_examples: 1
- name: test
num_bytes: 18269
num_examples: 1
download_size: 16336
dataset_size: 53367
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
GEM-submissions/lewtun__this-is-a-test-name__1655888220 | ---
benchmark: gem
type: prediction
submission_name: This is a test name
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test name
|
Siki-77/sst2 | ---
license: apache-2.0
---
|
visheratin/google_landmarks_places | ---
dataset_info:
features:
- name: id
dtype: int64
- name: category_name
dtype: string
- name: name
dtype: string
- name: lat
dtype: float64
- name: lon
dtype: float64
- name: city
dtype: string
- name: state
dtype: string
- name: country
dtype: string
splits:
- name: train
num_bytes: 4124162
num_examples: 35070
download_size: 2792316
dataset_size: 4124162
license: odbl
language:
- en
---
# Google Landmarks places
[Google Landmarks](https://storage.googleapis.com/gld-v2/web/index.html) is a great dataset, but it lacks geospatial information about the places. This dataset fills
this gap by providing latitude and longitude for each landmark. The dataset also contains the name of the landmark from [OpenStreetMap](https://www.openstreetmap.org/)
and information about the country, the province/state, and the city/village where the landmark is located. This information was collected from OSM
via [Nominatim](https://nominatim.org/). |
xinhaowang-hf/wiki-query | ---
dataset_info:
features:
- name: title
dtype: string
- name: context
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 1077887
num_examples: 629
- name: validation
num_bytes: 105746
num_examples: 60
- name: test
num_bytes: 109711
num_examples: 62
download_size: 839602
dataset_size: 1293344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
liuyanchen1015/MULTI_VALUE_cola_say_complementizer | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 3246
num_examples: 31
- name: test
num_bytes: 3095
num_examples: 33
- name: train
num_bytes: 16767
num_examples: 198
download_size: 17053
dataset_size: 23108
---
# Dataset Card for "MULTI_VALUE_cola_say_complementizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Rud/multi_lexsum_et_validation | ---
dataset_info:
features:
- name: id
dtype: string
- name: sources
sequence: string
- name: sources_metadata
sequence:
- name: doc_id
dtype: string
- name: doc_type
dtype: string
- name: doc_title
dtype: string
- name: parser
dtype: string
- name: is_ocr
dtype: bool
- name: url
dtype: string
- name: summary/long
dtype: string
- name: summary/short
dtype: string
- name: summary/tiny
dtype: string
- name: case_metadata
struct:
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- name: case_type
dtype: string
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dtype: string
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- name: class_action_sought
dtype: string
- name: class_action_granted
dtype: string
- name: attorney_orgs
list: string
- name: prevailing_party
dtype: string
- name: plaintiff_types
list: string
- name: plaintiff_description
dtype: string
- name: constitutional_clauses
list: string
- name: causes_of_action
list: string
- name: summary_authors
list: string
- name: case_url
dtype: string
- name: document
dtype: string
- name: et_summary
dtype: string
splits:
- name: validation
num_bytes: 227955865
num_examples: 454
download_size: 103412737
dataset_size: 227955865
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
CyberHarem/flint_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of flint/フリント/燧石 (Arknights)
This is the dataset of flint/フリント/燧石 (Arknights), containing 60 images and their tags.
The core tags of this character are `long_hair, blue_eyes, dark_skin, dark-skinned_female, hair_between_eyes, black_hair, bandaid_on_face, brown_hair, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 60 | 112.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flint_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 60 | 93.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flint_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 153 | 184.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/flint_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/flint_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, solo, long_sleeves, looking_at_viewer, black_shirt, smile, closed_mouth, hair_over_one_eye, bandaid, black_gloves, fingerless_gloves, open_jacket, purple_flower, white_skirt, simple_background, upper_body, white_background |
| 1 | 17 |  |  |  |  |  | 1girl, black_pantyhose, crop_top, midriff, solo, navel, bandaged_arm, looking_at_viewer, torn_pantyhose, denim_shorts, short_shorts, wrist_wrap, bandaid, cutoffs, simple_background, white_shirt, cowboy_shot, hood_up, stomach, standing, white_background, black_belt, boxing_gloves, jacket, shoes, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | long_sleeves | looking_at_viewer | black_shirt | smile | closed_mouth | hair_over_one_eye | bandaid | black_gloves | fingerless_gloves | open_jacket | purple_flower | white_skirt | simple_background | upper_body | white_background | black_pantyhose | crop_top | midriff | navel | bandaged_arm | torn_pantyhose | denim_shorts | short_shorts | wrist_wrap | cutoffs | white_shirt | cowboy_shot | hood_up | stomach | standing | black_belt | boxing_gloves | jacket | shoes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------------------|:--------------|:--------|:---------------|:--------------------|:----------|:---------------|:--------------------|:--------------|:----------------|:--------------|:--------------------|:-------------|:-------------------|:------------------|:-----------|:----------|:--------|:---------------|:-----------------|:---------------|:---------------|:-------------|:----------|:--------------|:--------------|:----------|:----------|:-----------|:-------------|:----------------|:---------|:--------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 17 |  |  |  |  |  | X | X | | X | | X | | | X | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
ibranze/araproje_arc_en_f5 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: validation
num_bytes: 80031.0
num_examples: 250
download_size: 0
dataset_size: 80031.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_arc_en_f5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
espoir/congo-cd-news-dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: summary
dtype: 'null'
- name: posted_at
dtype: timestamp[ns]
- name: website_origin
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 132575998.87505311
num_examples: 118623
- name: test
num_bytes: 33144279.124946892
num_examples: 29656
download_size: 97840693
dataset_size: 165720278.0
---
# Dataset Card for "congo-cd-news-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nguyenthanhdo/orca-unanswerable-vi | ---
dataset_info:
features:
- name: id
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 79650895
num_examples: 27280
download_size: 39058385
dataset_size: 79650895
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "orca-unanswerable-vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hardikch05/100000_text_to_sql | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 103993344739
num_examples: 78131251
download_size: 27852811712
dataset_size: 103993344739
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gorkemgoknar/tr_ted_talk_translated | ---
language:
- tr
thumbnail:
tags:
- dataset
- turkish
- ted-multi
- cleaned
license: apache-2.0
datasets:
- ted-multi
---
# Turkish Ted talk translations
# Created from ted-multi dataset
adding processing steps here if you want another language
```python
#using Turkish as target
target_lang="tr" # change to your target lang
from datasets import load_dataset
#ted-multi is a multiple language translated dataset
#fits for our case , not to big and curated but need a simple processing
dataset = load_dataset("ted_multi")
dataset.cleanup_cache_files()
#original from patrick's
#chars_to_ignore_regex = '[,?.!\-\;\:\"“%‘”�—’…–]' # change to the ignored characters of your fine-tuned model
#will use cahya/wav2vec2-base-turkish-artificial-cv
#checking inside model repository to find which chars removed (no run.sh)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
import re
def extract_target_lang_entries(batch):
#specific mapping for ted_multi dataset
#need to find index of language in each translation as it can shift
try:
target_index_for_lang= batch["translations"]["language"].index(target_lang)
except ValueError:
#target not in list empty it for later processing
batch["text"] = None
return batch
#index_translation_pairs = zip(batch, target_index_for_batch)
text= batch["translations"]["translation"][target_index_for_lang]
batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
return batch
#this dataset has additional columns need to say it
cols_to_remove = ['translations', 'talk_name']
dataset = dataset.map(extract_target_lang_entries, remove_columns=cols_to_remove)
#on preocessing we tagged None for empty ones
dataset_cleaned = dataset.filter(lambda x: x['text'] is not None)
dataset_cleaned
from huggingface_hub import notebook_login
notebook_login()
dataset_cleaned.push_to_hub(f"{target_lang}_ted_talk_translated")
``` |
Vishnu1/fyi | ---
license: cc-by-nc-4.0
---
|
keysol/Frp | ---
license: openrail
---
|
Doub7e/SDv2-Spatial-Repeated-3 | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
- name: T5_last_hidden_states
sequence:
sequence:
sequence: float32
- name: style
dtype: string
splits:
- name: train
num_bytes: 1202829006.0
num_examples: 1000
download_size: 1036362532
dataset_size: 1202829006.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jahb57/gpt2_sentence_embeddings | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 3446878
num_examples: 20
download_size: 4068897
dataset_size: 3446878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AI4EPS/quakeflow_das | ---
license: mit
---
This repository contains datasets for [PhaseNet-DAS](https://arxiv.org/abs/2302.08747). The DAS event format is explained here: [Seismic Event Format for DAS](https://ai4eps.github.io/homepage/ml4earth/seismic_event_format_das/)
- **Ridgrecrest**
The "ridgecrest_north" dataset is extracted from [**The SCEDC Earthquake Data AWS Public Dataset**](https://scedc.caltech.edu/data/cloud.html).
This dataset is collected by Prof. Zhongwen Zhan (zwzhan@caltech.edu). Please inform the authors if you utilize this dataset in your research.
- **Eureka**
The "eureka" dataset is extracted from [**Spring 2022 Arcata to Eureka California, Distributed Acoustic Sensing (DAS) experimentt**](https://www.sciencebase.gov/catalog/item/62fd776fd34e3a444286cd54).
This dataset is collected by Jeffrey J McGuire (jmcguire@usgs.gov) and Andrew J Barbour (abarbour@usgs.gov). Please inform the authors if you utilize this dataset in your research.
Note the "eureka" dataset uses a different hdf5 format of OptaSense. |
yzhuang/autotree_automl_electricity_gosdt_d3 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float64
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: int64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float64
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 554000000
num_examples: 10000
- name: validation
num_bytes: 55400000
num_examples: 1000
download_size: 157301353
dataset_size: 609400000
---
# Dataset Card for "autotree_automl_electricity_gosdt_d3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
valashir/SMM2-levels-simple-v2 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: level
sequence:
sequence:
sequence: uint8
- name: text
dtype: string
- name: text-baseline
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 11203379275.0
num_examples: 202096
- name: val
num_bytes: 112694381.0
num_examples: 2048
download_size: 1274247910
dataset_size: 11316073656.0
---
# Dataset Card for "SMM2-levels-simple-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hoshangc/google_search_terms_training_data | ---
task_categories:
- text-classification
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
**Dataset Name**: Google Search Trends Top Rising Search Terms
**Description**:
The Google Search Trends Top Rising Search Terms dataset provides valuable insights into the most rapidly growing search queries on the Google search engine. It offers a comprehensive collection of trending search queries, their search frequencies, and relevant metadata. Researchers and data enthusiasts can utilize this dataset to analyze search trends, identify emerging topics, and gain a deeper understanding of user interests that are currently on the rise. Whether for market research, content optimization, or data-driven decision-making, this dataset offers a wealth of information to explore the dynamic landscape of online search behavior, highlighting what is gaining popularity in real-time.
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
songys/Ko_humane_right_copus | ---
license: cc-by-sa-3.0
---
# HRC: Building a human rights corpus for interactive generation models
#대화형 생성 모델을 위한 인권코퍼스 구축
## 참조 데이터
- 대한민국 [국가인권위원회](https://case.humanrights.go.kr/dici/diciList.do)의 결정례와 상담사례 참조
- 문체 변경과 질의 응답으로 변경하기 위해서 전후 맥락을 고려한 예시문을 만들고 GPT-3.5-turbo 을 이용하여 원샷 학습후 문답 생성
## 데이터 구조
- 데이터 구조 : source_copus---counsel.jsonl
---decision.jsonl
humane_right_copus_v1.jsonl
## 프롬프트 예시
```
[상담례 prompt]
주어진 상담 문서를 자연스러운 질문, 답변 형태로 변형해 주세요.
답변이 끝나면 '#####'를 작성해 주세요.
반드시 원본의 답변 내용을 기반으로 답변해야 합니다.
질문은 최대한 간결하게 작성해 주세요.
#####
상담 내용: 시청 앞 광장에서 노동조합이 기자회견을 하고 있습니다. 그런데 경찰 세 개 중대가 기자회견장을 에워싸고 집회를 해산하라는 방송을 계속 내보내고 있습니다. 우리는 기자회견을 하고 있으며 설령 집회라 하더라도 평화적으로 하는 집회에 대해 해산 명령을 하는 것은 부당합니다.
원본 상담 답변: 신고를 하지 아니하였다는 이유만으로 개최가 허용되지 않는 집회 내지 시위라고 단정할 수 없습니다. 기자회견이 개인 발언 및 구호 제창 등 미신고 옥외집회의 성격을 띠었다고 하더라도 헌법상 표현의 자유와 집회 시위의 자유라는 민주주의의 근간을 이루는 국민의 기본권이 최대한 보장되도록 해야 합니다. 참고로 대법원(2012도9776)은 “기자회견이 집회에 해당한다 하더라도, 해산명령불응죄가 성립하려면 집회로 인해 타인의 법익이나 공공의 안녕질서에 대한 직접적인 위험이 명백하게 초래된 경우에 해당”하는 것으로 판단한 바 있습니다. 다만, 기자회견이라는 이름으로 개최되고, 일부 기자회견의 성격이 가미되었다고 하더라도 집회로서의 성격을 부인할 수 없다면 신고의 의무까지 면제된다고 보기는 어려울 수 있습니다.
간결한 질문: 시청 앞 광장에서 노동조합이 기자회견을 진행 중입니다. 그런데 경찰이 기자회견장을 둘러싸고 해산하라는 방송을 내보내고 있습니다. 이런 상황에서 경찰의 해산 명령은 정당한 것인가요?
답변: 신고를 하지 않은 집회가 허용되지 않는다는 단순한 이유만으로 기자회견을 미신고 옥외집회로 판단하는 것은 부적절합니다. 기자회견도 표현의 자유와 집회 시위의 자유, 민주주의의 핵심적인 국민의 기본권에 해당하므로 최대한 보장되어야 합니다. 대법원의 판례(2012도9776)에 따르면, "기자회견이 집회에 해당한다 해도, 그 집회로 인해 다른 사람의 법적 이익이나 공공 안녕질서에 대한 명백한 위험이 발생해야 해산명령불응죄가 성립한다"고 하였습니다. 그렇지만, 기자회견이 집회의 성격을 함께 갖는 경우 신고 의무를 면제받는 것은 아닙니다.
#####
상담 내용: {}
원본 상담 답변: {}
간결한 질문:
[결정례 prompt]
주어진 요청, 근거, 결론을 참고하여, 새로운 법률 관련 질문과 답변을 만들어주세요.
답변은 500자 이내로 작성해주세요.
반드시 주어진 자료의 사실을 활용해야 합니다.
답변 끝나면, '*****' 를 작성해주세요.
*****
요청:
진정인은 사회복무요원으로 피진정기관에서 업무 보조 및 폐의약품 수거 일을 하였다. 2021. 7. 26.폐의약품 수거를 위해 진정인의 자전거를 타고 인도 위를 지나다 뛰어오던 행인을 치는 교통사고를 내어 벌금형40만을 선고받았다.교통사고 피해자는 전치 6주 진단을 받았고, 1,30만원가량의 국가배상 청구를 신청한 상태이며, 피진정인은 진정인에게중과실 책임이 있기 때문에 구상권을 청구할 수 있다고 한다.진정인이 인도에서 자전거를 운행한 것은 잘못이지만, 공무 중 발생한 사고에 대해 사회복무요원이 모든 책임을 지는 것은 부당하다. 피진정기관이 진정인에게 구상권을 행사하지 않도록 도와주기 바란다.
근거:
1. 진정인이 자전거로 인도를 횡단하는 등 중과실 책임이 있긴 하나, 사전에 피진정인이 복무관리기관의 장으로서 주의 의무를 다하였다면 진정인이 교통사고에 이르지 않았을 수 있고, 20대 초반의 사회초년생이 벌금을 비롯해 고액의 손해배상 부담을 지는 상황을 피할 수 있었을 것임. 2. 사회복무요원은 병역의무를 이행하고자 국가기관, 지방자치단체 등에 소속되어 공익목적 업무 수행 차원에서 사회서비스 및 행정업무에 복무하고 있는 자이고, 「병역법」 제31조에서도 사회복무요원의 직무상 행위를 공무수행으로 규정하고 있는 바, 공무수행 중 발생한 사고에 대하여 사회복무요원을 공무원 등과 달리 대우할 합리적인 이유가 없음. 따라서 법적 지원체계 마련이 필요함. 3. 진정 취지가 구상권을 행사하지 않도록 해달라는 요청이므로, 이는 헌법상 권리 침해 여부를 판단할 사안이 아님.
결론:
주문 1 : 1. 광명시장에게, 안전사고 위험이 큰 분야에 소속 사회복무요원을 가급적 배치하지 않도록 하고, 배치가 불가피할 때에는 사전에 안전교육을 철저히 실시하도록 의견을 표명합니다. 주문 2 : 2. 병무청장에게, 사회복무요원의 복무 관련 사고 발생 시 초기단계부터 지원하고 민형사상 부담을 최소화하는 지원체계를 마련하도록 의견을 표명합니다. 주문 3 : 3. 이 사건 진정은 각하합니다.
위를 참고한 새로운 질문과 답변
질문: 사회복무요원이 공무 중 발생한 교통사고로 인한 피해자에 대한 구상권을 행사할 수 있는지에 대한 법적 근거가 어떻게 되는지 알려주세요.
답변: 사회복무요원이 공무 중 발생한 교통사고로 인한 피해자에 대한 구상권 여부는 법적으로 인정됩니다. 이는 병역법 제31조에 따라 사회복무요원의 직무를 공무수행으로 규정하고 있으며, 중과실 책임이 있더라도 사전에 안전 조치를 취하거나 교통법규를 준수해 사고를 예방할 의무가 있음을 의미합니다. 따라서 피해자는 피사회복무요원에 대한 손해배상을 청구할 수 있습니다.
*****
요청:
{}
근거:
{}
결론:
{}
위를 참고한 새로운 질문과 답변
질문:
```
## 인용
```
@inproceedings{song2023},
author = {송영숙 and 심상진 and 김성현},
title = {대화형 생성 모델을 위한 인권 코퍼스 구축},
booktitle = {한글 및 한국어 정보처리 학술대회 발표 예정)},
year = {2023},
publisher = {한글 및 한국어 정보처리 학회}
}
```
|
ngxingyu/iwslt17_google_trans_scores | ---
dataset_info:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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path: data/validation-*
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path: data/test-*
---
|
Henrl/minhavoz | ---
license: openrail
---
|
adeocybersecurity/DockerCommand | ---
license: apache-2.0
---
|
eurecom-ds/scoresdeve_activations_resnets_shapes3d | ---
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data_files:
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path: t_0.9_up_blocks.4.resnets.1.conv1/train-*
- split: test
path: t_0.9_up_blocks.4.resnets.1.conv1/test-*
- config_name: t_0.9_up_blocks.4.resnets.1.conv2
data_files:
- split: train
path: t_0.9_up_blocks.4.resnets.1.conv2/train-*
- split: test
path: t_0.9_up_blocks.4.resnets.1.conv2/test-*
- config_name: t_0.9_up_blocks.4.resnets.1.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.4.resnets.1.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.4.resnets.1.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.4.resnets.2.conv1
data_files:
- split: train
path: t_0.9_up_blocks.4.resnets.2.conv1/train-*
- split: test
path: t_0.9_up_blocks.4.resnets.2.conv1/test-*
- config_name: t_0.9_up_blocks.4.resnets.2.conv2
data_files:
- split: train
path: t_0.9_up_blocks.4.resnets.2.conv2/train-*
- split: test
path: t_0.9_up_blocks.4.resnets.2.conv2/test-*
- config_name: t_0.9_up_blocks.4.resnets.2.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.4.resnets.2.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.4.resnets.2.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.4.skip_conv
data_files:
- split: train
path: t_0.9_up_blocks.4.skip_conv/train-*
- split: test
path: t_0.9_up_blocks.4.skip_conv/test-*
- config_name: t_0.9_up_blocks.5.resnet_up.conv1
data_files:
- split: train
path: t_0.9_up_blocks.5.resnet_up.conv1/train-*
- split: test
path: t_0.9_up_blocks.5.resnet_up.conv1/test-*
- config_name: t_0.9_up_blocks.5.resnet_up.conv2
data_files:
- split: train
path: t_0.9_up_blocks.5.resnet_up.conv2/train-*
- split: test
path: t_0.9_up_blocks.5.resnet_up.conv2/test-*
- config_name: t_0.9_up_blocks.5.resnet_up.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.5.resnet_up.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.5.resnet_up.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.5.resnets.0.conv1
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.0.conv1/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.0.conv1/test-*
- config_name: t_0.9_up_blocks.5.resnets.0.conv2
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.0.conv2/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.0.conv2/test-*
- config_name: t_0.9_up_blocks.5.resnets.0.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.0.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.0.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.5.resnets.1.conv1
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.1.conv1/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.1.conv1/test-*
- config_name: t_0.9_up_blocks.5.resnets.1.conv2
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.1.conv2/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.1.conv2/test-*
- config_name: t_0.9_up_blocks.5.resnets.1.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.1.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.1.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.5.resnets.2.conv1
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.2.conv1/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.2.conv1/test-*
- config_name: t_0.9_up_blocks.5.resnets.2.conv2
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.2.conv2/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.2.conv2/test-*
- config_name: t_0.9_up_blocks.5.resnets.2.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.5.resnets.2.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.5.resnets.2.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.5.skip_conv
data_files:
- split: train
path: t_0.9_up_blocks.5.skip_conv/train-*
- split: test
path: t_0.9_up_blocks.5.skip_conv/test-*
- config_name: t_0.9_up_blocks.6.resnets.0.conv1
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.0.conv1/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.0.conv1/test-*
- config_name: t_0.9_up_blocks.6.resnets.0.conv2
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.0.conv2/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.0.conv2/test-*
- config_name: t_0.9_up_blocks.6.resnets.0.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.0.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.0.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.6.resnets.1.conv1
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.1.conv1/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.1.conv1/test-*
- config_name: t_0.9_up_blocks.6.resnets.1.conv2
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.1.conv2/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.1.conv2/test-*
- config_name: t_0.9_up_blocks.6.resnets.1.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.1.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.1.conv_shortcut/test-*
- config_name: t_0.9_up_blocks.6.resnets.2.conv1
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.2.conv1/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.2.conv1/test-*
- config_name: t_0.9_up_blocks.6.resnets.2.conv2
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.2.conv2/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.2.conv2/test-*
- config_name: t_0.9_up_blocks.6.resnets.2.conv_shortcut
data_files:
- split: train
path: t_0.9_up_blocks.6.resnets.2.conv_shortcut/train-*
- split: test
path: t_0.9_up_blocks.6.resnets.2.conv_shortcut/test-*
---
|
WorkWithData/Country_Covid19_Daily_2022 | ---
license: cc-by-4.0
---
This dataset shows daily cases and deaths from Covid-19 by country in 2020. The dataset can also be found on: https://www.workwithdata.com/dataset?entity=covid_country_daily&f=1&fcol0=date&fop0=includes&fval0=2022
Similar datasets can be found on: https://www.workwithdata.com |
mikeg2/eu | ---
license: openrail
---
|
bgspaditya/mal-url-treat-no-trunc | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: url
dtype: string
- name: type
dtype: string
- name: type_code
dtype: int64
splits:
- name: train
num_bytes: 42427560.8
num_examples: 512900
- name: val
num_bytes: 5303403.7395390915
num_examples: 64112
- name: test
num_bytes: 5303486.460460909
num_examples: 64113
download_size: 32110906
dataset_size: 53034451.0
---
# Dataset Card for "mal-url-treat-no-trunc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BitTranslate/chatgpt-prompts-Japanese | ---
license: cc0-1.0
---
|
thanhduycao/soict_sentence_filter | ---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 12169
num_examples: 197
download_size: 6583
dataset_size: 12169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "soict_sentence_filter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AlexHung29629/oscar-en-10k-megds | ---
task_categories:
- text-generation
language:
- en
size_categories:
- 1K<n<10K
---
# README
This dataset is used to test the training of the LLAMA model with Megatron-Deepspeed.
## Source
stas/oscar-en-10k
## Command
```bash
python tools/preprocess_data.py \
--input oscar-en-10k.jsonl \
--tokenizer-type PretrainedFromHF \
--append-eod \
--tokenizer-name-or-path hf-internal-testing/llama-tokenizer \
--output-prefix oscar-en-10k
```
## Info
\#tokens = 34030076 |
hugfaceguy0001/LightNovelInfo | ---
dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: author
dtype: string
- name: introduction
dtype: string
- name: publisher
dtype: string
- name: length
dtype: string
splits:
- name: train
num_bytes: 2544581
num_examples: 3523
download_size: 1869762
dataset_size: 2544581
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: openrail
task_categories:
- text-classification
- question-answering
- summarization
- text2text-generation
- sentence-similarity
language:
- zh
tags:
- literature
- art
pretty_name: LightNovel
size_categories:
- 1K<n<10K
---
# 轻小说信息数据集
整理了轻小说文库(wenku8)网站上三千多部日本轻小说的信息，包括"id"(轻小说文库中的小说原编号)，"title"(标题)，"author"（作者），"introduction"（小说简介），"publisher"(文库分类)，"length"(字数统计)六个
字段。
可用于信息检索，文本生成等任务。 |
jslin09/Fraud_Case_Verdicts | ---
license: apache-2.0
language:
- zh
task_categories:
- text-generation
tags:
- legal
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: test
path: test.csv
- split: validate
path: validate.csv
size_categories:
- 100M<n<1B
---
# The "Crime Facts" of "Offenses of Fraudulence" in Judicial Yuan Verdicts Dataset
This data set is based on the judgments of "Offenses of Fraudulence" cases published by the Judicial Yuan. The data range of the dataset is from January 1, 2011, to December 31, 2021. 74,823 pieces of original data (judgments and rulings) were collected. We only took the contents of the "criminal facts" field of the judgment. This dataset is divided into three parts. The training dataset has 59,858 verdicts, accounting for about 80% of the original data. The remaining 20% is allocated 10% to the verification (7,482 verdicts) and 10% to the test (7,483 verdicts). "Criminal facts" have been Chinese word segmented. If word segmentation is not needed, please merge it yourself.
# 司法院「詐欺罪」判決書「犯罪事實」資料集
本資料集是以司法院公開之「詐欺」案件判決書做成之資料集。資料集之資料範圍從100年1月1日至110年12月31日，所蒐集到的原始資料共有 74823 篇（判決以及裁定），我們只取判決書的「犯罪事實」欄位內容，並把這原始的資料分成三份，用於訓練的資料集有59858篇，約佔原始資料的80%，剩下的20%，則是各分配10%給驗證集（7482篇），10%給測試集（7483篇）。「犯罪事實」已經經過斷詞，如果不需要斷詞，請自行合併。 |
autoevaluate/autoeval-staging-eval-project-87e7c3be-9085195 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: dbounds/roberta-large-finetuned-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: dbounds/roberta-large-finetuned-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mxnno](https://huggingface.co/mxnno) for evaluating this model. |
CyberHarem/qbu_88_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of qbu_88/QBU-88/QBU-88 (Girls' Frontline)
This is the dataset of qbu_88/QBU-88/QBU-88 (Girls' Frontline), containing 21 images and their tags.
The core tags of this character are `long_hair, black_hair, bangs, yellow_eyes, brown_eyes, hairband, hair_bun, ribbon, breasts, brown_hair, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 21 | 25.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/qbu_88_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 21 | 15.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/qbu_88_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 46 | 28.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/qbu_88_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 21 | 22.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/qbu_88_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 46 | 38.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/qbu_88_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/qbu_88_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, black_gloves, blush, fingerless_gloves, rifle, white_shirt, long_sleeves, solo, double_bun, dress, hair_ribbon, holding_gun, looking_at_viewer, open_coat, shoes, white_background, yellow_ribbon, black_footwear, black_skirt, collared_shirt, full_body, fur_trim, open_mouth, simple_background, socks, thigh_strap, yellow_hairband |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | blush | fingerless_gloves | rifle | white_shirt | long_sleeves | solo | double_bun | dress | hair_ribbon | holding_gun | looking_at_viewer | open_coat | shoes | white_background | yellow_ribbon | black_footwear | black_skirt | collared_shirt | full_body | fur_trim | open_mouth | simple_background | socks | thigh_strap | yellow_hairband |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------|:--------------------|:--------|:--------------|:---------------|:-------|:-------------|:--------|:--------------|:--------------|:--------------------|:------------|:--------|:-------------------|:----------------|:-----------------|:--------------|:-----------------|:------------|:-----------|:-------------|:--------------------|:--------|:--------------|:------------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
klimbat85/AnthonyEdwards | ---
license: afl-3.0
---
|
Arjun1234/Arjun | ---
license: apache-2.0
---
|
adumitrescu18/Financial10kBenchmark | ---
license: mit
---
# Dataset Card for 10-K Benchmark
## Dataset Description
This is a synthetically generated financial benchmark for large language models based off of a wide range of 10-K documents. The benchmark was subsequently run on multiple LLMs, which provided candidate answers for evaluation based on both the context and the generated question.
The original 10-K dataset contains annual reports of public US companies that filed with the SEC / the EDGAR system from 1993-2020. The link to the original dataset used for context extraction can be found [here](https://huggingface.co/datasets/JanosAudran/financial-reports-sec/blob/main/README.md).
GPT-4 was then used in conjunction with these pieces of extracted content to automatically generate an adverserial dataset of complex and challenging questions covering calculations, financial domain expertise and legal / regulatory knowledge. A wide variety of models including GPT-4-Turbo, GPT-3.5-Turbo, Llama-7b-chat-hf, Llama-2-70b-chat-hf, Zephyr-7b-Beta, Solar-10.7b-Instruct-v1.0, Mistral-7B-Instruct-v0.2, Mixtral-8x7B-Instruct-v0.1 were then used to answer the synthetically generated questions using both the "golden context" provided AND their own knowledge.
Lastly, GPT-4 was used to evaluate the correctness of each answer that was generated by each of the models. GPT-4 provided (1) a binary "Yes/No" label (which is shared in the dataset), and (2) an accompanying justification (which was manually reviewed by our team, but not included in the dataset).
For full details on the dataset processing and overall procedure / findings of this study, please see the accompanying [Medium article](https://medium.com/@samirdutta523/58eade03bb26).
### Usage
To load the dataset, run:
```python
from datasets import load_dataset
ds = load_dataset("adumitrescu18/Financial10kBenchmark")
```
To filter the dataset results by model used (in this case ChatGPT 3.5 Turbo), run:
```python
from datasets import load_dataset
ds = load_dataset("adumitrescu18/Financial10kBenchmark")
ds = ds["train"].filter(lambda x: x["Model Evaluated"] == "ChatGPT 3.5 Turbo")
```
### Data Fields
The dataset has 8 different fields and attributes:
| cik | filingDate | docID | generatedQuestion | context | Model Response | Model Evaluated | Model Evaluation |
|:-------:|:-----------:|:-----:| :-----:| :-----:| :-----:| :-----:| :-----:|
| 10 digit identifier used by SEC for a firm | The date the 10-K report was filed with the SEC | Unique ID for identifying the exact 10-K filing. Unique across all configs and splits. Can be used to identify the document from which the sentence came from. | The question generated from GPT-4 | Sections taken from the dataset to give llms context for question answering | Response of the llm given a question and context | Name of the Model that was used for question answering | GPT-4 Evaluation of the models response | |
AMead10/Universal-glaive-function-calling-v2 | ---
dataset_info:
features:
- name: system
dtype: string
- name: conversation
list:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 251613737
num_examples: 112960
download_size: 99291787
dataset_size: 251613737
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Universal-glaive-function-calling-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MohammedNasri/Denoised_data_jason4 | ---
dataset_info:
features:
- name: data
struct:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: 'null'
- name: sampling_rate
dtype: int64
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1078347063
num_examples: 2000
download_size: 265545890
dataset_size: 1078347063
---
# Dataset Card for "Denoised_data_jason4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
VASVASVAS/lora_not_mine | ---
license: openrail
---
|
jubba/nano_nextgpt_instruct | ---
license: apache-2.0
---
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_125m_Attributes_ns_10 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_1_bs_16
num_bytes: 123008.0
num_examples: 10
download_size: 0
dataset_size: 123008.0
---
# Dataset Card for "OxfordPets_test_facebook_opt_125m_Attributes_ns_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
matrix-multiply/EnduRL_data | ---
license: mit
---
|
DeepFoldProtein/foldseek_combined_processed_BPE100_512 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: special_tokens_mask
sequence: int8
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6296503008
num_examples: 876462
download_size: 795343698
dataset_size: 6296503008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tiennguyenbnbk/03F_IEMOCAP | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: label
dtype: int64
- name: input_values
sequence: float32
splits:
- name: train
num_bytes: 1462828704
num_examples: 4995
- name: val
num_bytes: 147505532
num_examples: 536
download_size: 1220025832
dataset_size: 1610334236
---
# Dataset Card for "03F_IEMOCAP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aim143/guanaco-llama2-500 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 812365
num_examples: 500
download_size: 477144
dataset_size: 812365
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
davanstrien/test_embeddings | ---
dataset_info:
features:
- name: crawl_date
dtype: int64
- name: last_modified_date
dtype: float64
- name: url
dtype: string
- name: filename
dtype: string
- name: extension
dtype: string
- name: mime_type_web_server
dtype: string
- name: mime_type_tika
dtype: string
- name: width
dtype: int64
- name: height
dtype: int64
- name: md5
dtype: string
- name: sha1
dtype: string
- name: image
dtype: image
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 210083085.0
num_examples: 969
download_size: 208374121
dataset_size: 210083085.0
---
# Dataset Card for "test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HuggingFaceM4/ScienceQAImg_Modif_NoMCQ | Invalid username or password. |
skrishna/allenai-real-toxicity-prompts_70M_non_toxic | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 87904
num_examples: 500
- name: test
num_bytes: 81265
num_examples: 500
download_size: 120920
dataset_size: 169169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "allenai-real-toxicity-prompts_70M_non_toxic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jilp00/animal-behavior-transcripts | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 712262
num_examples: 492
download_size: 393197
dataset_size: 712262
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
malucoelhaofc/JoeLockeV2 | ---
license: openrail
---
|
Edopangui/promo2_parquet | ---
license: apache-2.0
---
|
lsb/poetaexmachina-recitations-onegrams | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 6909913371.324
num_examples: 176826
download_size: 6026737051
dataset_size: 6909913371.324
---
# Dataset Card for "poetaexmachina-recitations-onegrams"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
thangvip/orca-filter-half-open | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 636502840.4529436
num_examples: 655016
download_size: 338685611
dataset_size: 636502840.4529436
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "orca-filter-half-open"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Aderek514/comfyui | ---
license: other
license_name: comfyui
license_link: LICENSE
---
The archive contains a comfyuui-portable with uploaded nods.
ComfyUI is great, however you have to download many different add-ons. You can access my kit here.
Links to WF running on this kit:
https://civitai.com/articles/3451/deep-cache-lcm-and-sdxl-is-so-fast
https://civitai.com/articles/3517/improved-face-generation
https://civitai.com/user/Aderek514/articles
You have to change file extra_model_paths.yaml to yours setting, because there are mine
"base_path: C:/Users/Aderek/stable-diffusion-webui/"
How it loaded after unpack on my PC:
l:\ComfyUI_windows_portable>.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
** ComfyUI startup time: 2023-12-28 08:56:33.501942
** Platform: Windows
** Python version: 3.10.9 (tags/v3.10.9:1dd9be6, Dec 6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]
** Python executable: l:\ComfyUI_windows_portable\python_embeded\python.exe
** Log path: l:\ComfyUI_windows_portable\comfyui.log
Prestartup times for custom nodes:
0.0 seconds: L:\ComfyUI_windows_portable\ComfyUI\custom_nodes\rgthree-comfy
0.3 seconds: L:\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-Manager
Total VRAM 8191 MB, total RAM 32649 MB
xformers version: 0.0.23.post1+cu118
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 3060 Ti : cudaMallocAsync
VAE dtype: torch.bfloat16
Using xformers cross attention |
declare-lab/CategoricalHarmfulQA | ---
configs:
- config_name: default
data_files:
- split: en
path:
- data/catqa_english.json
- split: zh
path:
- data/catqa_chinese.json
- split: vi
path:
- data/catqa_vietnamese.json
license: apache-2.0
language:
- en
- zh
- vi
size_categories:
- 1K<n<10K
---
---
license: apache-2.0
---
## CatQA: A categorical harmful questions dataset
- CatQA is used in LLM safety realignment research:
__Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic__ ([Paper](https://arxiv.org/abs/2402.11746#:~:text=Safety%20Re%2DAlignment%20of%20Fine%2Dtuned%20Language%20Models%20through%20Task%20Arithmetic,-Rishabh%20Bhardwaj%2C%20Do&text=Aligned%20language%20models%20face%20a,that%20performs%20LLM%20safety%20realignment.), [Code](https://github.com/declare-lab/resta/tree/main))
## How to download
```
from datasets import load_dataset
dataset = load_dataset("declare-lab/CategoricalHarmfulQA")
```
## What is CatQA?
To comprehensively evaluate the model across a wide range of harmful categories, we construct a new safety evaluation dataset CatQA. We obtain a combined list of prohibited use cases mentioned in OpenAI usage policies and Meta’s Llama2 acceptable use policy. As shown in the table below, there are 11 main categories of harm we consider. We divide each of these categories into 5 sub-categories. For each sub-category, we frame 10 harmful questions. Each sub-category and question is collected by us with the assistance of a powerful but unaligned LLM (the LLM is helpful but not safety-aligned. We do not disclose the name of LLM to prevent its misuse}. Thus, the dataset consists of 550 harmful questions, 55 such questions are shown in the table. While CatQA questions are in English, to evaluate the generalizability of our approach, we extend the dataset to two more languages: Chinese and Vietnamese. First, we translated the English version to the corresponding language using the LLM which assisted us in generating the former version, and employed language-proficient human annotators to enhance the quality of the dataset.
<img width="897" alt="catqa" src="https://github.com/declare-lab/resta/assets/32847115/6f5f1ba5-1b35-4181-bb05-0995ed1982fc">
## Citation
```bibtex
@misc{bhardwaj2024language,
title={Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic},
author={Rishabh Bhardwaj and Do Duc Anh and Soujanya Poria},
year={2024},
eprint={2402.11746},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
JovialValley/broadclass_totaldataset_1 | ---
dataset_info:
features:
- name: name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype: string
- name: emotion
dtype: string
- name: emotion_str
dtype: string
splits:
- name: train
num_bytes: 163909442.0
num_examples: 389
- name: test
num_bytes: 40149082.0
num_examples: 98
download_size: 137402351
dataset_size: 204058524.0
---
# Dataset Card for "broadclass_totaldataset_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DmitrMakeev/test_set | ---
license: openrail
---
|
mtkinit/Super22 | ---
pretty_name: Super22
tags:
- esa
---
# Super22
Created from AIOD platform |
gnitpmet/beatriz12 | ---
license: openrail
---
|
MahmoudAly/LexAbSumm | ---
license: afl-3.0
task_categories:
- summarization
- text-generation
- sentence-similarity
language:
- en
tags:
- legal
size_categories:
- 1K<n<10K
---
# Dataset Card for LexAbSumm
### Dataset Summary
Legal professionals frequently encounter long legal judgments that hold critical insights for their work. While recent advances have led to automated summarization solutions for legal documents, they typically provide generic summaries, which may not meet the diverse information needs of users. To address this gap, we introduce LexAbSumm, a novel dataset designed for aspect-based summarization of legal case decisions, sourced from the European Court of Human Rights jurisdiction. We evaluate several abstractive summarization models tailored for longer documents on LexAbSumm, revealing a challenge in conditioning these models to produce aspect-specific summaries. We release LexAbSum to facilitate research in aspect-based summarization for legal domain.
### Languages
English
# Citation Information |
hk-kaden-kim/uzh-hs23-etsp-eval-single-noaxislabel-bar | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: test
num_bytes: 4710127.0
num_examples: 100
download_size: 4650739
dataset_size: 4710127.0
---
# Dataset Card for "uzh-hs23-etsp-eval-single-noaxislabel-bar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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