davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
autoevaluate/autoeval-eval-squad-plain_text-47f200-2254171821	--- type: predictions tags: - autotrain - evaluation datasets: - squad eval_info: task: extractive_question_answering model: Palak/google_electra-small-discriminator_squad metrics: [] dataset_name: squad dataset_config: plain_text dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Palak/google_electra-small-discriminator_squad * Dataset: squad * Config: plain_text * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Biryukovlex](https://huggingface.co/Biryukovlex) for evaluating this model.
heliosprime/twitter_dataset_1713147118	--- 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: 4395 num_examples: 12 download_size: 8734 dataset_size: 4395 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713147118" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_mtgv__MobileLLaMA-1.4B-Base	--- pretty_name: Evaluation run of mtgv/MobileLLaMA-1.4B-Base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mtgv/MobileLLaMA-1.4B-Base](https://huggingface.co/mtgv/MobileLLaMA-1.4B-Base)\ \ 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_mtgv__MobileLLaMA-1.4B-Base\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-06T23:22:22.302402](https://huggingface.co/datasets/open-llm-leaderboard/details_mtgv__MobileLLaMA-1.4B-Base/blob/main/results_2024-04-06T23-22-22.302402.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\": 0.2537476149311213,\n\ \ \"acc_stderr\": 0.03071237430286461,\n \"acc_norm\": 0.2549089996364952,\n\ \ \"acc_norm_stderr\": 0.03147631325439452,\n \"mc1\": 0.21664626682986537,\n\ \ \"mc1_stderr\": 0.014421468452506985,\n \"mc2\": 0.3481107244797803,\n\ \ \"mc2_stderr\": 0.013684586182211824\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.310580204778157,\n \"acc_stderr\": 0.013522292098053054,\n\ \ \"acc_norm\": 0.3438566552901024,\n \"acc_norm_stderr\": 0.013880644570156215\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.42949611631149176,\n\ \ \"acc_stderr\": 0.004939925958728871,\n \"acc_norm\": 0.5629356701852221,\n\ \ \"acc_norm_stderr\": 0.004950095555964667\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.26666666666666666,\n\ \ \"acc_stderr\": 0.03820169914517905,\n \"acc_norm\": 0.26666666666666666,\n\ \ \"acc_norm_stderr\": 0.03820169914517905\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.18421052631578946,\n \"acc_stderr\": 0.0315469804508223,\n\ \ \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.28,\n\ \ \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.22264150943396227,\n \"acc_stderr\": 0.025604233470899098,\n\ \ \"acc_norm\": 0.22264150943396227,\n \"acc_norm_stderr\": 0.025604233470899098\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.24305555555555555,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.24305555555555555,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.16,\n \"acc_stderr\": 0.0368452949177471,\n \"acc_norm\"\ : 0.16,\n \"acc_norm_stderr\": 0.0368452949177471\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.20809248554913296,\n\ \ \"acc_stderr\": 0.030952890217749888,\n \"acc_norm\": 0.20809248554913296,\n\ \ \"acc_norm_stderr\": 0.030952890217749888\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.31063829787234043,\n \"acc_stderr\": 0.03025123757921317,\n\ \ \"acc_norm\": 0.31063829787234043,\n \"acc_norm_stderr\": 0.03025123757921317\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.03999423879281336,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.03999423879281336\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131184,\n\ \ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131184\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2857142857142857,\n \"acc_stderr\": 0.02326651221373057,\n \"\ acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.02326651221373057\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.29365079365079366,\n\ \ \"acc_stderr\": 0.04073524322147125,\n \"acc_norm\": 0.29365079365079366,\n\ \ \"acc_norm_stderr\": 0.04073524322147125\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\"\ : 0.26129032258064516,\n \"acc_stderr\": 0.024993053397764815,\n \"\ acc_norm\": 0.26129032258064516,\n \"acc_norm_stderr\": 0.024993053397764815\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.24630541871921183,\n \"acc_stderr\": 0.03031509928561773,\n \"\ acc_norm\": 0.24630541871921183,\n \"acc_norm_stderr\": 0.03031509928561773\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.23636363636363636,\n \"acc_stderr\": 0.03317505930009181,\n\ \ \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.03317505930009181\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.21717171717171718,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.21717171717171718,\n \"acc_norm_stderr\": 0.029376616484945633\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.20725388601036268,\n \"acc_stderr\": 0.029252823291803613,\n\ \ \"acc_norm\": 0.20725388601036268,\n \"acc_norm_stderr\": 0.029252823291803613\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.21025641025641026,\n \"acc_stderr\": 0.020660597485026924,\n\ \ \"acc_norm\": 0.21025641025641026,\n \"acc_norm_stderr\": 0.020660597485026924\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.22962962962962963,\n \"acc_stderr\": 0.025644108639267624,\n \ \ \"acc_norm\": 0.22962962962962963,\n \"acc_norm_stderr\": 0.025644108639267624\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.19747899159663865,\n \"acc_stderr\": 0.02585916412205146,\n\ \ \"acc_norm\": 0.19747899159663865,\n \"acc_norm_stderr\": 0.02585916412205146\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.24503311258278146,\n \"acc_stderr\": 0.03511807571804723,\n \"\ acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.03511807571804723\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.22568807339449543,\n \"acc_stderr\": 0.01792308766780305,\n \"\ acc_norm\": 0.22568807339449543,\n \"acc_norm_stderr\": 0.01792308766780305\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.23148148148148148,\n \"acc_stderr\": 0.028765111718046948,\n \"\ acc_norm\": 0.23148148148148148,\n \"acc_norm_stderr\": 0.028765111718046948\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604257,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604257\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658342,\n \ \ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.34080717488789236,\n\ \ \"acc_stderr\": 0.031811497470553604,\n \"acc_norm\": 0.34080717488789236,\n\ \ \"acc_norm_stderr\": 0.031811497470553604\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.03768335959728744,\n\ \ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.03768335959728744\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2644628099173554,\n \"acc_stderr\": 0.04026187527591207,\n \"\ acc_norm\": 0.2644628099173554,\n \"acc_norm_stderr\": 0.04026187527591207\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.2767857142857143,\n\ \ \"acc_stderr\": 0.042466243366976256,\n \"acc_norm\": 0.2767857142857143,\n\ \ \"acc_norm_stderr\": 0.042466243366976256\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n\ \ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.02934311479809447,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.02934311479809447\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.2796934865900383,\n\ \ \"acc_stderr\": 0.016050792148036553,\n \"acc_norm\": 0.2796934865900383,\n\ \ \"acc_norm_stderr\": 0.016050792148036553\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.26011560693641617,\n \"acc_stderr\": 0.023618678310069367,\n\ \ \"acc_norm\": 0.26011560693641617,\n \"acc_norm_stderr\": 0.023618678310069367\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.20915032679738563,\n \"acc_stderr\": 0.023287685312334806,\n\ \ \"acc_norm\": 0.20915032679738563,\n \"acc_norm_stderr\": 0.023287685312334806\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.24437299035369775,\n\ \ \"acc_stderr\": 0.024406162094668886,\n \"acc_norm\": 0.24437299035369775,\n\ \ \"acc_norm_stderr\": 0.024406162094668886\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\"\ : 0.2553191489361702,\n \"acc_stderr\": 0.026011992930901996,\n \"\ acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.026011992930901996\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2405475880052151,\n\ \ \"acc_stderr\": 0.010916406735478949,\n \"acc_norm\": 0.2405475880052151,\n\ \ \"acc_norm_stderr\": 0.010916406735478949\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.37272727272727274,\n \"acc_stderr\": 0.04631381319425463,\n\ \ \"acc_norm\": 0.37272727272727274,\n \"acc_norm_stderr\": 0.04631381319425463\n\ \ },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.1836734693877551,\n\ \ \"acc_stderr\": 0.02478907133200765,\n \"acc_norm\": 0.1836734693877551,\n\ \ \"acc_norm_stderr\": 0.02478907133200765\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.21393034825870647,\n \"acc_stderr\": 0.028996909693328906,\n\ \ \"acc_norm\": 0.21393034825870647,\n \"acc_norm_stderr\": 0.028996909693328906\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n\ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370519,\n\ \ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370519\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.29239766081871343,\n\ \ \"acc_stderr\": 0.03488647713457922,\n \"acc_norm\": 0.29239766081871343,\n\ \ \"acc_norm_stderr\": 0.03488647713457922\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 0.21664626682986537,\n \"mc1_stderr\": 0.014421468452506985,\n\ \ \"mc2\": 0.3481107244797803,\n \"mc2_stderr\": 0.013684586182211824\n\ \ },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.5943172849250198,\n\ \ \"acc_stderr\": 0.013800206336014207\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.011372251705837756,\n \"acc_stderr\": 0.0029206661987887465\n\ \ }\n}\n```" repo_url: https://huggingface.co/mtgv/MobileLLaMA-1.4B-Base leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|arc:challenge\|25_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-06T23-22-22.302402.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|gsm8k\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hellaswag\|10_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T23-22-22.302402.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-06T23-22-22.302402.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '/details_harness\|winogrande\|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-06T23-22-22.302402.parquet' - config_name: results data_files: - split: 2024_04_06T23_22_22.302402 path: - results_2024-04-06T23-22-22.302402.parquet - split: latest path: - results_2024-04-06T23-22-22.302402.parquet --- # Dataset Card for Evaluation run of mtgv/MobileLLaMA-1.4B-Base <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mtgv/MobileLLaMA-1.4B-Base](https://huggingface.co/mtgv/MobileLLaMA-1.4B-Base) 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_mtgv__MobileLLaMA-1.4B-Base", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-06T23:22:22.302402](https://huggingface.co/datasets/open-llm-leaderboard/details_mtgv__MobileLLaMA-1.4B-Base/blob/main/results_2024-04-06T23-22-22.302402.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": 0.2537476149311213, "acc_stderr": 0.03071237430286461, "acc_norm": 0.2549089996364952, "acc_norm_stderr": 0.03147631325439452, "mc1": 0.21664626682986537, "mc1_stderr": 0.014421468452506985, "mc2": 0.3481107244797803, "mc2_stderr": 0.013684586182211824 }, "harness\|arc:challenge\|25": { "acc": 0.310580204778157, "acc_stderr": 0.013522292098053054, "acc_norm": 0.3438566552901024, "acc_norm_stderr": 0.013880644570156215 }, "harness\|hellaswag\|10": { "acc": 0.42949611631149176, "acc_stderr": 0.004939925958728871, "acc_norm": 0.5629356701852221, "acc_norm_stderr": 0.004950095555964667 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909284, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909284 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.26666666666666666, "acc_stderr": 0.03820169914517905, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.03820169914517905 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.18421052631578946, "acc_stderr": 0.0315469804508223, "acc_norm": 0.18421052631578946, "acc_norm_stderr": 0.0315469804508223 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.22264150943396227, "acc_stderr": 0.025604233470899098, "acc_norm": 0.22264150943396227, "acc_norm_stderr": 0.025604233470899098 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.24305555555555555, "acc_stderr": 0.0358687928008034, "acc_norm": 0.24305555555555555, "acc_norm_stderr": 0.0358687928008034 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.16, "acc_stderr": 0.0368452949177471, "acc_norm": 0.16, "acc_norm_stderr": 0.0368452949177471 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.26, "acc_stderr": 0.044084400227680794, "acc_norm": 0.26, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749888, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749888 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.31063829787234043, "acc_stderr": 0.03025123757921317, "acc_norm": 0.31063829787234043, "acc_norm_stderr": 0.03025123757921317 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.23684210526315788, "acc_stderr": 0.03999423879281336, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.03999423879281336 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.22758620689655173, "acc_stderr": 0.03493950380131184, "acc_norm": 0.22758620689655173, "acc_norm_stderr": 0.03493950380131184 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.02326651221373057, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.02326651221373057 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147125, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147125 }, "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": 0.26129032258064516, "acc_stderr": 0.024993053397764815, "acc_norm": 0.26129032258064516, "acc_norm_stderr": 0.024993053397764815 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.24630541871921183, "acc_stderr": 0.03031509928561773, "acc_norm": 0.24630541871921183, "acc_norm_stderr": 0.03031509928561773 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.23636363636363636, "acc_stderr": 0.03317505930009181, "acc_norm": 0.23636363636363636, "acc_norm_stderr": 0.03317505930009181 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.21717171717171718, "acc_stderr": 0.029376616484945633, "acc_norm": 0.21717171717171718, "acc_norm_stderr": 0.029376616484945633 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.20725388601036268, "acc_stderr": 0.029252823291803613, "acc_norm": 0.20725388601036268, "acc_norm_stderr": 0.029252823291803613 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.21025641025641026, "acc_stderr": 0.020660597485026924, "acc_norm": 0.21025641025641026, "acc_norm_stderr": 0.020660597485026924 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.22962962962962963, "acc_stderr": 0.025644108639267624, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.025644108639267624 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.19747899159663865, "acc_stderr": 0.02585916412205146, "acc_norm": 0.19747899159663865, "acc_norm_stderr": 0.02585916412205146 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.24503311258278146, "acc_stderr": 0.03511807571804723, "acc_norm": 0.24503311258278146, "acc_norm_stderr": 0.03511807571804723 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.22568807339449543, "acc_stderr": 0.01792308766780305, "acc_norm": 0.22568807339449543, "acc_norm_stderr": 0.01792308766780305 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.23148148148148148, "acc_stderr": 0.028765111718046948, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.028765111718046948 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604257, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604257 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.26582278481012656, "acc_stderr": 0.028756799629658342, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.028756799629658342 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.34080717488789236, "acc_stderr": 0.031811497470553604, "acc_norm": 0.34080717488789236, "acc_norm_stderr": 0.031811497470553604 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.24427480916030533, "acc_stderr": 0.03768335959728744, "acc_norm": 0.24427480916030533, "acc_norm_stderr": 0.03768335959728744 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2644628099173554, "acc_stderr": 0.04026187527591207, "acc_norm": 0.2644628099173554, "acc_norm_stderr": 0.04026187527591207 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.044143436668549335, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.044143436668549335 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.24539877300613497, "acc_stderr": 0.03380939813943354, "acc_norm": 0.24539877300613497, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.2767857142857143, "acc_stderr": 0.042466243366976256, "acc_norm": 0.2767857142857143, "acc_norm_stderr": 0.042466243366976256 }, "harness\|hendrycksTest-management\|5": { "acc": 0.2524271844660194, "acc_stderr": 0.04301250399690877, "acc_norm": 0.2524271844660194, "acc_norm_stderr": 0.04301250399690877 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02934311479809447, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02934311479809447 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2796934865900383, "acc_stderr": 0.016050792148036553, "acc_norm": 0.2796934865900383, "acc_norm_stderr": 0.016050792148036553 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.26011560693641617, "acc_stderr": 0.023618678310069367, "acc_norm": 0.26011560693641617, "acc_norm_stderr": 0.023618678310069367 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.20915032679738563, "acc_stderr": 0.023287685312334806, "acc_norm": 0.20915032679738563, "acc_norm_stderr": 0.023287685312334806 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.24437299035369775, "acc_stderr": 0.024406162094668886, "acc_norm": 0.24437299035369775, "acc_norm_stderr": 0.024406162094668886 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.25, "acc_stderr": 0.02409347123262133, "acc_norm": 0.25, "acc_norm_stderr": 0.02409347123262133 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2553191489361702, "acc_stderr": 0.026011992930901996, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.026011992930901996 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2405475880052151, "acc_stderr": 0.010916406735478949, "acc_norm": 0.2405475880052151, "acc_norm_stderr": 0.010916406735478949 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.18382352941176472, "acc_stderr": 0.023529242185193106, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.023529242185193106 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.37272727272727274, "acc_stderr": 0.04631381319425463, "acc_norm": 0.37272727272727274, "acc_norm_stderr": 0.04631381319425463 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.1836734693877551, "acc_stderr": 0.02478907133200765, "acc_norm": 0.1836734693877551, "acc_norm_stderr": 0.02478907133200765 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.21393034825870647, "acc_stderr": 0.028996909693328906, "acc_norm": 0.21393034825870647, "acc_norm_stderr": 0.028996909693328906 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370519, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370519 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.29239766081871343, "acc_stderr": 0.03488647713457922, "acc_norm": 0.29239766081871343, "acc_norm_stderr": 0.03488647713457922 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.21664626682986537, "mc1_stderr": 0.014421468452506985, "mc2": 0.3481107244797803, "mc2_stderr": 0.013684586182211824 }, "harness\|winogrande\|5": { "acc": 0.5943172849250198, "acc_stderr": 0.013800206336014207 }, "harness\|gsm8k\|5": { "acc": 0.011372251705837756, "acc_stderr": 0.0029206661987887465 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
jjuarez/autotrain-data-fine-tune	--- task_categories: - image-classification --- # AutoTrain Dataset for project: fine-tune ## Dataset Description This dataset has been automatically processed by AutoTrain for project fine-tune. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<382x256 RGB PIL image>", "target": 17 }, { "image": "<341x256 RGB PIL image>", "target": 7 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['apple', 'banana', 'cake', 'candy', 'carrot', 'cookie', 'doughnut', 'grape', 'hot dog', 'ice cream', 'juice', 'muffin', 'orange', 'pineapple', 'popcorn', 'pretzel', 'salad', 'strawberry', 'waffle', 'watermelon'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 5394 \| \| valid \| 1351 \|
makeouthill0071/xxxtentacion	--- license: openrail ---
Gabriel1322/joao	--- license: openrail ---
alexthomas4/highsub-classified-images	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Abullah '1': Adolf '2': Anya '3': Asta '4': Atom '5': Becky '6': Bond '7': Brando '8': Damian '9': Duncan '10': Epsilon '11': Franky '12': Gauche '13': Gesicht '14': Heracles '15': Kazuki '16': Kyutaro '17': Loid '18': Mimosa '19': Miri '20': Misaki '21': Nightfall '22': 'North #2' '23': Ochanomizu '24': Pluto '25': Rei '26': Side Character '27': Taiga '28': Tenma '29': Uran '30': Yor '31': Yuno '32': Yuri splits: - name: train num_bytes: 54171170.0 num_examples: 410 download_size: 54191470 dataset_size: 54171170.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
deetsadi/processed_dwi_sobel_all_b_values_large_mask_with_lesion	--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: conditioning_image dtype: image splits: - name: train num_bytes: 41930844.0 num_examples: 200 download_size: 41926762 dataset_size: 41930844.0 --- # Dataset Card for "processed_dwi_sobel_all_b_values_large_mask_with_lesion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/firewatch_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of firewatch/ファイヤーウォッチ/守林人 (Arknights) This is the dataset of firewatch/ファイヤーウォッチ/守林人 (Arknights), containing 154 images and their tags. The core tags of this character are `horns, long_hair, grey_hair, sidelocks, ponytail, green_eyes, 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 \| 154 \| 275.14 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/firewatch_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 154 \| 231.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/firewatch_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 390 \| 447.02 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/firewatch_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/firewatch_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 \| 25 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, long_sleeves, solo, open_jacket, black_gloves, black_skirt, closed_mouth, crossbow, green_jacket, looking_at_viewer, pleated_skirt, reindeer_antlers, outdoors, animal_ears, black_footwear, holding_gun, standing, green_pantyhose, collared_shirt, full_body, rifle, shoes, brown_eyes \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, antlers, green_jacket, long_sleeves, looking_at_viewer, open_jacket, solo, black_gloves, black_skirt, simple_background, white_background, backpack, collared_shirt, hand_up, pleated_skirt, brown_eyes, grey_eyes, official_alternate_costume, pantyhose, parted_lips, speech_bubble, sweater, upper_body \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, closed_mouth, solo, upper_body, open_jacket, collared_shirt, simple_background, animal_ears, reindeer_antlers, smile, hood_down, long_sleeves, looking_at_viewer, white_background \| \| 3 \| 40 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, antlers, solo, bare_shoulders, looking_at_viewer, black_gloves, sleeveless_dress, white_dress, black_thighhighs, hairband, simple_background, white_background, bare_arms, yellow_eyes, closed_mouth, crossbow, bag \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| long_sleeves \| solo \| open_jacket \| black_gloves \| black_skirt \| closed_mouth \| crossbow \| green_jacket \| looking_at_viewer \| pleated_skirt \| reindeer_antlers \| outdoors \| animal_ears \| black_footwear \| holding_gun \| standing \| green_pantyhose \| collared_shirt \| full_body \| rifle \| shoes \| brown_eyes \| antlers \| simple_background \| white_background \| backpack \| hand_up \| grey_eyes \| official_alternate_costume \| pantyhose \| parted_lips \| speech_bubble \| sweater \| upper_body \| smile \| hood_down \| bare_shoulders \| sleeveless_dress \| white_dress \| black_thighhighs \| hairband \| bare_arms \| yellow_eyes \| bag \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-------\|:--------------\|:---------------\|:--------------\|:---------------\|:-----------\|:---------------\|:--------------------\|:----------------\|:-------------------\|:-----------\|:--------------\|:-----------------\|:--------------\|:-----------\|:------------------\|:-----------------\|:------------\|:--------\|:--------\|:-------------\|:----------\|:--------------------\|:-------------------\|:-----------\|:----------\|:------------\|:-----------------------------\|:------------\|:--------------\|:----------------\|:----------\|:-------------\|:--------\|:------------\|:-----------------\|:-------------------\|:--------------\|:-------------------\|:-----------\|:------------\|:--------------\|:------\| \| 0 \| 25 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| \| \| X \| \| \| X \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| 3 \| 40 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| \| X \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \|
ai4bharat/IndicQuestionGeneration	--- annotations_creators: - no-annotation language_creators: - found language: - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te license: - cc-by-nc-4.0 multilinguality: - multilingual pretty_name: IndicQuestionGeneration size_categories: - 98K<n<98K source_datasets: - we start with the SQuAD question answering dataset repurposed to serve as a question generation dataset. We translate this dataset into different Indic languages. task_categories: - conditional-text-generation task_ids: - conditional-text-generation-other-question-generation --- # Dataset Card for "IndicQuestionGeneration" ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://indicnlp.ai4bharat.org/indicnlg-suite - Paper: [IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://arxiv.org/abs/2203.05437) - Point of Contact: ### Dataset Summary IndicQuestionGeneration is the question generation dataset released as part of IndicNLG Suite. Each example has five fields: id, squad_id, answer, context and question. We create this dataset in eleven languages, including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. This is translated data. The examples in each language are exactly similar but in different languages. The number of examples in each language is 98,027. ### Supported Tasks and Leaderboards Tasks: Question Generation Leaderboards: Currently there is no Leaderboard for this dataset. ### Languages - `Assamese (as)` - `Bengali (bn)` - `Gujarati (gu)` - `Kannada (kn)` - `Hindi (hi)` - `Malayalam (ml)` - `Marathi (mr)` - `Oriya (or)` - `Punjabi (pa)` - `Tamil (ta)` - `Telugu (te)` ## Dataset Structure ### Data Instances One random example from the `hi` dataset is given below in JSON format. ``` { "id": 8, "squad_id": "56be8e613aeaaa14008c90d3", "answer": "अमेरिकी फुटबॉल सम्मेलन", "context": "अमेरिकी फुटबॉल सम्मेलन (एएफसी) के चैंपियन डेनवर ब्रोंकोस ने नेशनल फुटबॉल कांफ्रेंस (एनएफसी) की चैंपियन कैरोलिना पैंथर्स को 24-10 से हराकर अपना तीसरा सुपर बाउल खिताब जीता।", "question": "एएफसी का मतलब क्या है?" } ``` ### Data Fields - `id (string)`: Unique identifier. - `squad_id (string)`: Unique identifier in Squad dataset. - `answer (strings)`: Answer as one of the two inputs. - `context (string)`: Context, the other input. - `question (string)`: Question, the output. ### Data Splits Here is the number of samples in each split for all the languages. Language \| ISO 639-1 Code \| Train \| Dev \| Test \| ---------- \| ---------- \| ---------- \| ---------- \| ---------- \| Assamese \| as \| 69,979 \| 17,495 \| 10,553 \| Bengali \| bn \| 69,979 \| 17,495 \| 10,553 \| Gujarati \| gu \| 69,979 \| 17,495 \| 10,553 \| Hindi \| hi \| 69,979 \| 17,495 \| 10,553 \| Kannada \| kn \| 69,979 \| 17,495 \| 10,553 \| Malayalam \| ml \| 69,979 \| 17,495 \| 10,553 \| Marathi \| mr \| 69,979 \| 17,495 \| 10,553 \| Oriya \| or \| 69,979 \| 17,495 \| 10,553 \| Punjabi \| pa \| 69,979 \| 17,495 \| 10,553 \| Tamil \| ta \| 69,979 \| 17,495 \| 10,553 \| Telugu \| te \| 69,979 \| 17,495 \| 10,553 \| ## Dataset Creation ### Curation Rationale [Detailed in the paper](https://arxiv.org/abs/2203.05437) ### Source Data Squad Dataset(https://rajpurkar.github.io/SQuAD-explorer/) #### Initial Data Collection and Normalization [Detailed in the paper](https://arxiv.org/abs/2203.05437) #### Who are the source language producers? [Detailed in the paper](https://arxiv.org/abs/2203.05437) ### Annotations [More information needed] #### 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 Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders. ### Citation Information If you use any of the datasets, models or code modules, please cite the following paper: ``` @inproceedings{Kumar2022IndicNLGSM, title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages}, author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar}, year={2022}, url = "https://arxiv.org/abs/2203.05437", ``` ### Contributions [Detailed in the paper](https://arxiv.org/abs/2203.05437)
jganzabalseenka/direct_tv_vectors	--- dataset_info: features: - name: cluster_frames sequence: sequence: int64 - name: cluster_vectors sequence: sequence: float64 - name: cluster_predictions sequence: int64 - name: distances_between_clusters sequence: sequence: float64 - name: video_path dtype: string - name: different_rows list: - name: black_image dtype: bool - name: frame_number dtype: int64 - name: height dtype: int64 - name: horizontal_check dtype: bool - name: horizontal_xmax dtype: int64 - name: horizontal_xmin dtype: int64 - name: horizontal_ymax dtype: int64 - name: horizontal_ymin dtype: int64 - name: is_L_shape dtype: bool - name: vertical_check dtype: bool - name: vertical_xmax dtype: int64 - name: vertical_xmin dtype: int64 - name: vertical_ymax dtype: int64 - name: vertical_ymin dtype: int64 - name: width dtype: int64 splits: - name: train num_bytes: 50750 num_examples: 2 download_size: 0 dataset_size: 50750 --- # Dataset Card for "direct_tv_vectors" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
angry-snail/tcfd-v1	--- license: cc-by-nc-4.0 task_categories: - text-classification language: - en --- Dataset for TCFD complience
breadlicker45/wizards-of-Waverly-place-scripts	--- license: other ---
autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-79c1c0d8-10905463	--- type: predictions tags: - autotrain - evaluation datasets: - kmfoda/booksum eval_info: task: summarization model: pszemraj/led-large-book-summary metrics: ['bleu', 'perplexity'] dataset_name: kmfoda/booksum dataset_config: kmfoda--booksum dataset_split: test col_mapping: text: chapter target: summary_text --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/led-large-book-summary * Dataset: kmfoda/booksum * Config: kmfoda--booksum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
vsvasconcelos/SQuAD-pt_BR-V1.1_	--- license: mit language: - pt size_categories: - 100K<n<1M --- # Dataset Card para o SQuAD 1.1 em Português Brasil O conjunto de dados "Stanford Question Answering Dataset" ([SQuAD](https://drive.google.com/file/d/1Q0IaIlv2h2BC468MwUFmUST0EyN7gNkn/view)), para tarefa de perguntas e respostas extrativas, foi desenvolvido em 2016. Ele utiliza perguntas geradas a partir de 536 artigos da Wikipedia* com mais de 100.000 linhas de dados. É construído na forma de uma pergunta e um contexto dos artigos da Wikipedia contendo a resposta à pergunta. [[1]](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2761899.pdf) Originalmente este dataset foi construído no idioma inglês, contudo, o grupo [Deep Learning Brasil](http://www.deeplearningbrasil.com.br/) o traduziu automaticamente e fez os ajustes manuais, gastando para isto cerca de 2 meses. [[2]](https://sol.sbc.org.br/index.php/kdmile/article/view/24974) ## Dataset Details ### Dataset Description O desenvolvedores fornecem dois arquivos: squad-train-v1.1.json e squad-dev-v1.1.json, sendo o primeiro para treinamento e o segundo para validação. Os arquivos possuem, respectivamente, as seguintes quantidades de registros: 87.510 e 17.853, totalizando assim: 105.363 registros. Percentualmente isto equivale a 83% dos dados para treinamento e 17% dos dados para validação. Após Análise Exploratória do dataset, verificou-se a existência de 7.283 'Id's repetidos no conjunto de validação, assim, ao invés de 17.853 registros, o conjunto de validação possuí 10.570 'Id's únicos. Desta forma, os 7.283 'Id's repetidos foram excluídos do dataset de validação original. No universo de Machine Learning, é comum a divisão dos datasets em: Treinamento; Validação; e Testes. Assim, por que aqui não foi fornecido os dados de Testes? Pesquisando em [[1]](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2761899.pdf), temos: > "[...] Utilizamos 80% do conjunto de dados para treinar o modelo, 10% para validação e ajuste hiperparâmetro. Os 10% finais do conjunto > de dados são reservados para testes e mantidos privados pelos criadores da equipe com o objetivo de preservar a integridade dos > modelos de resposta a perguntas." Portanto, os autores não disponibilizaram os 10% de dados para testes. Sendo assim, este dataset faz os seguintes ajustes: Mantem os dados originais do conjunto de treinamento e dividi o conjunto de validação em duas partes: validação (5.500 registros (5,6%) dos dados) e testes (5070 registros (5,2%) dos dados). - 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 features: 'Id', 'title', 'context', 'question', 'ans_start', 'text' 'Id': identificador único 'title': Assunto do qual o contexto trata 'context': Texto que contém a resposta para a questão 'question': Questão respondida por meio do contexto 'ans_start': Posição inicial da resposta à questão 'text': Resposta à questão [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data Wikipedia #### 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]
fyang-rai/vlogo	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 429548.0 num_examples: 6 download_size: 406378 dataset_size: 429548.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vlogo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
transformer-001/github-issues	--- dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: milestone struct: - name: url dtype: string - name: html_url dtype: string - name: labels_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: description dtype: string - name: creator struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: open_issues dtype: int64 - name: closed_issues dtype: int64 - name: state dtype: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: due_on dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 18908112 num_examples: 5000 download_size: 5112946 dataset_size: 18908112 --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_147	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1072808020.0 num_examples: 210685 download_size: 1094423199 dataset_size: 1072808020.0 --- # Dataset Card for "chunk_147" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GoodBaiBai88/M3D-VQA	--- license: apache-2.0 ---
Thanmay/boolq-mr	--- dataset_info: features: - name: question dtype: string - name: answer dtype: bool - name: passage dtype: string - name: itv2 mr question dtype: string - name: itv2 mr passage dtype: string splits: - name: validation num_bytes: 6916121 num_examples: 3270 download_size: 3172220 dataset_size: 6916121 configs: - config_name: default data_files: - split: validation path: data/validation-* ---
Deojoandco/reddit-ah-dialog-annotations_v5	--- dataset_info: features: - name: url dtype: string - name: id dtype: string - name: num_comments dtype: int64 - name: name dtype: string - name: title dtype: string - name: body dtype: string - name: score dtype: int64 - name: upvote_ratio dtype: float64 - name: distinguished dtype: string - name: over_18 dtype: bool - name: created_utc dtype: int64 - name: comments list: - name: body dtype: string - name: created_utc dtype: float64 - name: distinguished dtype: string - name: id dtype: string - name: permalink dtype: string - name: score dtype: int64 - name: best_num_comments dtype: int64 - name: query dtype: string - name: dialog dtype: string - name: annotation_success dtype: bool - name: annotation_text dtype: string - name: turns_generated dtype: bool splits: - name: train num_bytes: 33860238 num_examples: 2921 - name: validation num_bytes: 3121972 num_examples: 293 - name: test num_bytes: 3378331 num_examples: 292 download_size: 23369920 dataset_size: 40360541 --- # Dataset Card for "reddit-ah-dialog-annotations_v5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mattyhatch/tomatoesSpoof1	--- dataset_info: features: - name: pixel_values dtype: image - name: label sequence: sequence: int64 splits: - name: train num_bytes: 673124095.0 num_examples: 557 download_size: 34937459 dataset_size: 673124095.0 --- # Dataset Card for "tomatoesSpoof1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
keremberke/blood-cell-object-detection	--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface - Biology --- <div align="center"> <img width="640" alt="keremberke/blood-cell-object-detection" src="https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['platelets', 'rbc', 'wbc'] ``` ### Number of Images ```json {'train': 255, 'test': 36, 'valid': 73} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/blood-cell-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3](https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface) ### Citation ``` @misc{ blood-cell-detection-1ekwu_dataset, title = { Blood Cell Detection Dataset }, type = { Open Source Dataset }, author = { Team Roboflow }, howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } }, url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-01-18 }, } ``` ### License Public Domain ### Dataset Summary This dataset was exported via roboflow.com on November 4, 2022 at 7:46 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 364 images. Cells are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) No image augmentation techniques were applied.
anistaluqdar/dudl	--- license: mit ---
Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized	--- license: cc-by-4.0 ---
AKAWIZ/telugu_asr_custom	--- license: unlicense ---
petar5r/evalo_faq	--- license: apache-2.0 ---
CyberHarem/ribeyrolles_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ribeyrolles/リベロール/利贝罗勒 (Girls' Frontline) This is the dataset of ribeyrolles/リベロール/利贝罗勒 (Girls' Frontline), containing 184 images and their tags. The core tags of this character are `long_hair, bangs, hair_between_eyes, very_long_hair, blue_eyes, white_hair, wavy_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 \| 184 \| 238.73 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 184 \| 128.86 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 443 \| 280.24 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 184 \| 207.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 443 \| 404.28 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_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/ribeyrolles_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 \| 7 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, bandaged_leg, bare_shoulders, dress, looking_at_viewer, sitting, solo, bandaged_neck, blood_bag, blush, intravenous_drip, simple_background, barefoot, white_background, wide_sleeves, closed_mouth, detached_sleeves, side_cutout, full_body, hand_up, parted_lips \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, bandaged_leg, intravenous_drip, looking_at_viewer, bandaged_neck, blood_bag, closed_mouth, holding_gun, simple_background, white_background, assault_rifle, blue_dress, blush, wide_sleeves, bare_shoulders \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, looking_at_viewer, simple_background, solo, white_background, bandaged_neck, blush, grey_eyes, grey_hair, closed_mouth, long_sleeves \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, navel, nipples, simple_background, white_background, cum_in_pussy, grey_hair, small_breasts, spread_legs, 1boy, after_sex, after_vaginal, completely_nude, cumdrip, hetero, on_back, open_mouth, shiny_skin, solo_focus, sweat, censored, closed_mouth, collarbone, heart-shaped_pupils, looking_at_viewer, nose_blush, penis, purple_eyes, tears, tongue_out, trembling \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bandaged_leg \| bare_shoulders \| dress \| looking_at_viewer \| sitting \| solo \| bandaged_neck \| blood_bag \| blush \| intravenous_drip \| simple_background \| barefoot \| white_background \| wide_sleeves \| closed_mouth \| detached_sleeves \| side_cutout \| full_body \| hand_up \| parted_lips \| holding_gun \| assault_rifle \| blue_dress \| grey_eyes \| grey_hair \| long_sleeves \| navel \| nipples \| cum_in_pussy \| small_breasts \| spread_legs \| 1boy \| after_sex \| after_vaginal \| completely_nude \| cumdrip \| hetero \| on_back \| open_mouth \| shiny_skin \| solo_focus \| sweat \| censored \| collarbone \| heart-shaped_pupils \| nose_blush \| penis \| purple_eyes \| tears \| tongue_out \| trembling \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-----------------\|:--------\|:--------------------\|:----------\|:-------\|:----------------\|:------------\|:--------\|:-------------------\|:--------------------\|:-----------\|:-------------------\|:---------------\|:---------------\|:-------------------\|:--------------\|:------------\|:----------\|:--------------\|:--------------\|:----------------\|:-------------\|:------------\|:------------\|:---------------\|:--------\|:----------\|:---------------\|:----------------\|:--------------\|:-------\|:------------\|:----------------\|:------------------\|:----------\|:---------\|:----------\|:-------------\|:-------------\|:-------------\|:--------\|:-----------\|:-------------\|:----------------------\|:-------------\|:--------\|:--------------\|:--------\|:-------------\|:------------\| \| 0 \| 7 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| \| X \| \| X \| X \| \| X \| \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| \| X \| \| \| \| \| \| \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
firstgradeai/private_processed_demo	--- dataset_info: features: - name: formatted_chat dtype: string splits: - name: train num_bytes: 134885 num_examples: 10 download_size: 78616 dataset_size: 134885 configs: - config_name: default data_files: - split: train path: data/train-* ---
mask-distilled-one-sec-cv12/chunk_170	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1237712440 num_examples: 243070 download_size: 1261211885 dataset_size: 1237712440 --- # Dataset Card for "chunk_170" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
A-Bar/nl-de_non_top_cs_train	--- dataset_info: features: - name: query dtype: string - name: passage dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 409205240 num_examples: 1000000 download_size: 173282815 dataset_size: 409205240 configs: - config_name: default data_files: - split: train path: data/train-* ---
hungnm/vietnamese-medical-qa	--- dataset_info: features: - name: answer dtype: string - name: question dtype: string splits: - name: train num_bytes: 10672406 num_examples: 9335 download_size: 5289086 dataset_size: 10672406 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - vi tags: - question answering - medical - vietnamese license: apache-2.0 pretty_name: b size_categories: - 1K<n<10K --- ## Dataset Summary Vietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from [edoctor](https://edoctor.io/hoi-dap) and [vinmec](https://www.vinmec.com/vi/tin-tuc/hoi-dap-bac-si/). * Size: After merging data from these two sources, obtained 9335 QA pairs. * Language: Vietnamese ## Load with Datasets ```python from datasets import load_dataset # Load dataset from huggingface qa_dataset = load_dataset("hungnm/vietnamese-medical-qa") # print a QA example print(qa_dataset['train'][0]) ``` ```json { "question": "Chào bác sĩ,\nRăng cháu hiện tại có mủ ở dưới lợi nhưng khi đau cháu sẽ không ngủ được (quá đau). Tuy nhiên chỉ vài ngày là hết mà thỉnh thoảng nó lại bị đau. Chị cháu bảo là trước chị cháu cũng bị như vậy chỉ là đau răng tuổi dậy thì thôi. Bác sĩ cho cháu hỏi đau răng kèm có mủ dưới lợi là bệnh gì? Cháu có cần đi chữa trị không? Cháu cảm ơn.", "answer": "Chào bạn,\nĐể trả lời câu hỏi trên, bác sĩ xin giải đáp như sau:\nRăng bạn hiện tại có mủ dưới lợi gây đau nhức nhiều. Bạn có thể đến phòng khám răng hàm mặt bệnh viện để được thăm khám, chụp phim và tư vấn cho bạn được chính xác\nTrân trọng!" } ``` ## Dataset Version and Update * Version Details: * Current version: 1.0 * Last Update: 16/02/2024 * First Release: 16/02/2024 * Update Plan: We will keep updating as we gather additional data. ## Licensing Information This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License. ## Citation Information ```bibtex @misc{vietnamese-medical-qa, title={Vietnamese Medical QA: Question Answering dataset for medical in Vietnamese}, author={Hung Nguyen}, year={2024}, } ```
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b6645655	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1340 dataset_size: 184 --- # Dataset Card for "b6645655" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
taln-ls2n/kpbiomed	--- annotations_creators: - unknown language_creators: - unknown language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual task_categories: - text-mining - text-generation task_ids: - keyphrase-generation - keyphrase-extraction size_categories: - 100K<n<1M pretty_name: KP-Biomed --- # KPBiomed, A Large-Scale Dataset for keyphrase generation ## About This dataset is made of 5.6 million abstracts with author assigned keyphrases. Details about the dataset can be found in the original paper: Maël Houbre, Florian Boudin and Béatrice Daille. 2022. [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://arxiv.org/abs/2211.12124). In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI 2022). Reference (author-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper: - Florian Boudin and Ygor Gallina. 2021. [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics. Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text. ## Content The details of the dataset are in the table below: \| Split \| # documents \| # keyphrases by document (average) \| % Present \| % Reordered \| % Mixed \| % Unseen \| \| :----------- \| ----------: \| ---------------------------------: \| --------: \| ----------: \| ------: \| -------: \| \| Train small \| 500k \| 5.24 \| 66.31 \| 7.16 \| 12.60 \| 13.93 \| \| Train medium \| 2M \| 5.24 \| 66.30 \| 7.18 \| 12.57 \| 13.95 \| \| Train large \| 5.6M \| 5.23 \| 66.32 \| 7.18 \| 12.55 \| 13.95 \| \| Validation \| 20k \| 5.25 \| 66.44 \| 7.07 \| 12.45 \| 14.05 \| \| Test \| 20k \| 5.22 \| 66.59 \| 7.22 \| 12.44 \| 13.75 \| The following data fields are available: - id: unique identifier of the document. - title: title of the document. - abstract: abstract of the document. - keyphrases: list of reference keyphrases. - mesh terms: list of indexer assigned MeSH terms if available (around 68% of the articles) - prmu: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases. - authors: list of the article's authors - year: publication year NB: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text).
met/Met	--- license: apache-2.0 ---
abwqr/pokemon_test	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4730788.764705882 num_examples: 33 download_size: 3699706 dataset_size: 4730788.764705882 --- # Dataset Card for "pokemon_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NathanRoll/TalkBank_CA_wM_Croatian	--- dataset_info: features: - name: audio sequence: float32 - name: text dtype: string - name: __index_level_0__ dtype: string splits: - name: train num_bytes: 5830164732 num_examples: 135 download_size: 5836483668 dataset_size: 5830164732 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "TalkBank_CA_wM_Croatian" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EPFL-LNMC/channelpedia_paper_taged	--- dataset_info: features: - name: metadata struct: - name: authors sequence: string - name: id dtype: string - name: paragraph_id dtype: int64 - name: section dtype: string - name: title dtype: string - name: text dtype: string - name: embeddings sequence: float32 - name: tags sequence: string splits: - name: train num_bytes: 43188192.79975021 num_examples: 10135 download_size: 0 dataset_size: 43188192.79975021 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "channelpedia_paper_taged" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k	--- pretty_name: Evaluation run of Open-Orca/LlongOrca-13B-16k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Open-Orca/LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T02:39:50.739204](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k/blob/main/results_2023-09-23T02-39-50.739204.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.24842701342281878,\n\ \ \"em_stderr\": 0.004425115813837482,\n \"f1\": 0.3159280620805379,\n\ \ \"f1_stderr\": 0.004388510945380163,\n \"acc\": 0.4434148948074197,\n\ \ \"acc_stderr\": 0.010487468726575147\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.24842701342281878,\n \"em_stderr\": 0.004425115813837482,\n\ \ \"f1\": 0.3159280620805379,\n \"f1_stderr\": 0.004388510945380163\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.12282031842304776,\n \ \ \"acc_stderr\": 0.009041108602874675\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7640094711917916,\n \"acc_stderr\": 0.011933828850275621\n\ \ }\n}\n```" repo_url: https://huggingface.co/Open-Orca/LlongOrca-13B-16k 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_08_23T10_31_15.034338 path: - '/details_harness\|arc:challenge\|25_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-23T10:31:15.034338.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T02_39_50.739204 path: - '/details_harness\|drop\|3_2023-09-23T02-39-50.739204.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-23T02-39-50.739204.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T02_39_50.739204 path: - '/details_harness\|gsm8k\|5_2023-09-23T02-39-50.739204.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-23T02-39-50.739204.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hellaswag\|10_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-23T10:31:15.034338.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_23T10_31_15.034338 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-23T10:31:15.034338.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T02_39_50.739204 path: - '/details_harness\|winogrande\|5_2023-09-23T02-39-50.739204.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-23T02-39-50.739204.parquet' - config_name: results data_files: - split: 2023_09_23T02_39_50.739204 path: - results_2023-09-23T02-39-50.739204.parquet - split: latest path: - results_2023-09-23T02-39-50.739204.parquet --- # Dataset Card for Evaluation run of Open-Orca/LlongOrca-13B-16k ## Dataset Description - Homepage: - Repository: https://huggingface.co/Open-Orca/LlongOrca-13B-16k - 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 [Open-Orca/LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T02:39:50.739204](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k/blob/main/results_2023-09-23T02-39-50.739204.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.24842701342281878, "em_stderr": 0.004425115813837482, "f1": 0.3159280620805379, "f1_stderr": 0.004388510945380163, "acc": 0.4434148948074197, "acc_stderr": 0.010487468726575147 }, "harness\|drop\|3": { "em": 0.24842701342281878, "em_stderr": 0.004425115813837482, "f1": 0.3159280620805379, "f1_stderr": 0.004388510945380163 }, "harness\|gsm8k\|5": { "acc": 0.12282031842304776, "acc_stderr": 0.009041108602874675 }, "harness\|winogrande\|5": { "acc": 0.7640094711917916, "acc_stderr": 0.011933828850275621 } } ``` ### 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]
neulab/tldr	--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - mit multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: DocPrompting-CoNaLa tags: - code-generation - doc retrieval - retrieval augmented generation --- ## Dataset Description - Repository: https://github.com/shuyanzhou/docprompting - Paper: [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf) ### Dataset Summary This is the natural language to bash generation dataset we harvested from the English subset of [`tldr`](https://github.com/tldr-pages/tldr) We split the dataset by bash commands. Every command in the dev and test set is held out from the training set. ### Supported Tasks and Leaderboards This dataset is used to evaluate code generations. ### Languages English - Bash ## Dataset Structure ```python dataset = load_dataset("neulab/tldr") DatasetDict({ train: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 6414 }) test: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 928 }) validation: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 1845 }) }) code_docs = load_dataset("neulab/docprompting-conala", "docs") DatasetDict({ train: Dataset({ features: ['doc_id', 'doc_content'], num_rows: 439064 }) }) ``` ### Data Fields train/dev/test: - nl: The natural language intent - cmd: The reference code snippet - question_id: the unique id of a question - oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split - cmd_name: the bash command of this code snippet - tldr_cmd_name: the bash command used in tldr github repo. The `cmd_name` and `tldr_cmd_name` can be different due to naming difference - manual_exist: whether the manual exists in https://manned.org - matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token. docs: - doc_id: the id of a doc - doc_content: the content of the doc ## Dataset Creation The dataset was curated from [`tldr`](https://github.com/tldr-pages/tldr). The project aims to provide frequent usage of bash commands with natural language intents. For more details, please check the repo. ### Citation Information ``` @article{zhou2022doccoder, title={DocCoder: Generating Code by Retrieving and Reading Docs}, author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham}, journal={arXiv preprint arXiv:2207.05987}, year={2022} } ```
mstz/isolet	--- language: - en tags: - isolet - tabular_classification - binary_classification - multiclass_classification - UCI pretty_name: Isolet size_categories: - 1K<n<10K task_categories: - tabular-classification configs: - isolet license: cc --- # Isolet The [Isolet dataset](https://archive-beta.ics.uci.edu/dataset/54/isolet) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). # Configurations and tasks \| Configuration \| Task \| Description \| \|-------------------\|---------------------------\|--------------------------\| \| isolet \| Multiclass classification \| What letter was uttered? \| # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/isolet", "isolet")["train"] ```
karan451/BMI-labeled-faced	--- license: mit ---
316usman/thematic4aembed	--- dataset_info: features: - name: text dtype: string - name: thematic dtype: string - name: sub-thematic dtype: string - name: country dtype: string - name: document_url dtype: string - name: source_url dtype: string splits: - name: train num_bytes: 44691831 num_examples: 62243 download_size: 14281929 dataset_size: 44691831 configs: - config_name: default data_files: - split: train path: data/train-* ---
recoilme/train_data_dataset	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 2757788954.15 num_examples: 9050 download_size: 2575413735 dataset_size: 2757788954.15 configs: - config_name: default data_files: - split: train path: data/train-* ---
junkim100/SIL-Demo	--- license: mit ---
huggingartists/bushido-zho	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/bushido-zho" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 0.195456 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/6e5b165de8561df37790229c26b25692.959x959x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/bushido-zho"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">BUSHIDO ZHO</div> <a href="https://genius.com/artists/bushido-zho"> <div style="text-align: center; font-size: 14px;">@bushido-zho</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/bushido-zho). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/bushido-zho") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|91\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/bushido-zho") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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Eskobar/NepaliHandSign	--- license: creativeml-openrail-m ---
notrichardren/misconceptions_tf	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: Topic dtype: string - name: Question dtype: string - name: Correct dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 181025 num_examples: 1703 download_size: 83862 dataset_size: 181025 --- # Dataset Card for "misconceptions_tf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nuratamton/StorySculptor_dataset	--- language: - en license: gpl-3.0 ---
vaishaal/ImageNetV2	--- license: mit ---
didi0di/KoChatGPT	--- license: unknown dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: tokens dtype: int64 splits: - name: train num_bytes: 4902713 num_examples: 12000 download_size: 2876495 dataset_size: 4902713 task_categories: - text-generation language: - ko tags: - instruction - SFT size_categories: - 10K<n<100K --- # Dataset Summary ## 1.2.1) 한국어 QA 데이터셋의 질문으로 답변 자동 생성(ChatGPT): 12,000개 - SFT 데이터: data/train - 다음 단어만 잘 생성하는 모델에서 질문에 '잘' 대답하는 모델 만들기 - Human labling이 필요하지만 ChatGPT 이용 - 질문을 입력으로 주고 ChatGPT로 답변 자동생성 ### 데이터셋 예시 ``` { "prompt": "술 먹고 싶어", "completion": "'술은 알코올이 함유된 음료수이며, 건강에 나쁜 영향을 미칠 수 있습니다. 따라서 술을 마시기 전에 본인의 건강 상태와 함께 적당한 양을 마시는 것이 중요합니다. 술을 마시기 전에 반드시 20세 이상인지 확인하시고, 규정된 양 이내에서 적절하게 즐기시길 바랍니다. 또한 술을 마시고 운전하는 것은 절대 금지하시기 바랍니다.", "tokens": 189 } ``` ## 1.2.2) langchain을 이용한 채팅데이터 자동생성(ChatGPT): 439개 - kochatgpt_1_SFT_conversation.jsonl : 한국어 질문 수집 데이터셋 - 다음 단어만 잘 생성하는 모델에서 질문에 '잘' 대답하는 모델 만들기 - ChatGPT로 사람과 AI가 대화를 주고받는 채팅 데이터 생성 ### 데이터셋 예시 ``` <<start conversation>> <사람>: 그렇군요. 무엇에 대해 이야기하고 싶으세요? <챗봇>: 궁금하신 주제에 대해 이야기하고 싶으신가요? 예를 들어 여행, 식품, 스포츠, 등등 다양한 토론에 참여할 수 있습니다. <사람>: 그래서 스포츠에 대해 이야기해볼까요? <챗봇>: 그렇습니다. 스포츠에 대해 말씀하시겠습니까? 스포츠는 다양한 종목과 관련된 주제로 비슷한 경기를 비교하거나 새로운 경기를 분석할 수 있습니다. 또한 스포츠 관련 소식과 업데이트 등을 제공할 수 있습니다. <사람>: 괜찮습니다. 소식이나 업데이트는 어떻게 받을 수 있나요? <챗봇>: 귀하는 스포츠 관련 소식과 업데이트를 접할 수 있는 다양한 방법을 사용할 수 있습니다. 예를 들면 스포츠 사이트, 스포츠 앱, 스포츠 유튜브 채널 등을 통해 스포츠 소식과 업데이트를 받을 수 있습니다. 또한 소셜 미디어에서도 스포츠 소식과 업데이트를 받을 수 있습니다. <사람>: 감사합니다. 이제 스포츠 소식과 업데이트를 받아볼 준비가 되었습니다. <챗봇>: 그렇게 되었군요. 스포츠 소식과 업데이트를 받기 위해 귀하가 해야 할 준비는 무엇인가요? 스포츠 사이트, 스포츠 앱, 스포츠 유튜브 채널, 소셜 미디어 등을 통해 스포츠 소식과 업데이트를 받기 위해 준비해야 합니다. ``` ## Data Source https://github.com/airobotlab/KoChatGPT/tree/main
BEE-spoke-data/govdocs1-txt-raw	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: section dtype: string - name: filename dtype: string - name: text dtype: string splits: - name: train num_bytes: 16101385278.039026 num_examples: 67984 - name: validation num_bytes: 894547719.9804866 num_examples: 3777 - name: test num_bytes: 894547719.9804866 num_examples: 3777 download_size: 7656656755 dataset_size: 17890480718 license: odc-by task_categories: - text-generation size_categories: - 10K<n<100K --- # Dataset Card for "govdocs1-txt-raw" Somewhere to put the raw txt files before filtering them Source info/page: https://digitalcorpora.org/corpora/file-corpora/files/ ``` @inproceedings{garfinkel2009bringing, title={Bringing Science to Digital Forensics with Standardized Forensic Corpora}, author={Garfinkel, Simson and Farrell, Paul and Roussev, Vassil and Dinolt, George}, booktitle={Digital Forensic Research Workshop (DFRWS) 2009}, year={2009}, address={Montreal, Canada}, url={https://digitalcorpora.org/corpora/file-corpora/files/} } ```
MartinDx/second	--- license: mit ---
antolin/codealpaca-filtered	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: nl dtype: string - name: cmd dtype: string splits: - name: train num_bytes: 611759.1279592449 num_examples: 2194 - name: test num_bytes: 175106.98831285586 num_examples: 628 - name: valid num_bytes: 87553.49415642793 num_examples: 314 download_size: 447020 dataset_size: 874419.6104285286 --- # Dataset Card for "codealpaca-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NaturalTeam/KoBART_TEST	--- license: unknown ---
mayalenE/simple-foc-discourse	--- license: mit ---
p1atdev/202312-at20240326-safe	--- dataset_info: features: - name: id dtype: int64 - name: copyright dtype: string - name: character dtype: string - name: artist dtype: string - name: general dtype: string - name: meta dtype: string - name: rating dtype: string - name: score dtype: int64 - name: created_at dtype: string splits: - name: train num_bytes: 741938657 num_examples: 1487516 download_size: 280384451 dataset_size: 741938657 configs: - config_name: default data_files: - split: train path: data/train-* ---
nroggendorff/nebulae	--- license: mit size_categories: - 1K<n<10K task_categories: - image-classification - unconditional-image-generation tags: - art dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 231315247.272 num_examples: 1657 download_size: 265723176 dataset_size: 231315247.272 configs: - config_name: default data_files: - split: train path: data/train-* --- ## Nebula Image Dataset ### Overview This dataset contains a collection of images depicting various types of nebulas. Nebulas are large, diffuse astronomical objects composed of ionized gases, dust, and plasma. They are often found near star-forming regions and are an important part of the life cycle of stars. ### Dataset Description The dataset includes about one hundred high-resolution images of different nebulas, such as emission nebulas, reflection nebulas, and planetary nebulas. The images were collected from public astronomical sources and have been carefully curated to ensure high quality and diversity. ### Data Format The dataset is stored in the Parquet file format, which is a columnar data format that provides efficient storage and query performance. The Parquet file contains a single column, `train`, which holds the file paths to the individual nebula images. ## Usage You can use this dataset for a variety of machine learning and computer vision tasks, such as: - Nebula classification - Nebula segmentation - Nebula generation or synthesis - Astronomical image processing and analysis To get started, cite the [datasets section](https://huggingface.co/datasets/nroggendorff/nebulae#datasets). ### License This dataset is licensed under the [MIT License](LICENSE). You are free to use, modify, and distribute the data as long as you provide attribution. ### Datasets ```py from datasets import load_dataset config.dataset_name = "nroggendorff/nebulae" dataset = load_dataset(config.dataset_name, split="image") ``` ### Acknowledgements The images in this dataset were collected from various public astronomical sources. We would like to thank the astronomers and organizations who have contributed to the advancement of our understanding of nebulas and other celestial phenomena.
shidowake/glaive-code-assistant-v1-sharegpt-format_split_12	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 10503837.603832223 num_examples: 6805 download_size: 5171549 dataset_size: 10503837.603832223 configs: - config_name: default data_files: - split: train path: data/train-* ---
heliosprime/twitter_dataset_1713215686	--- 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: 18980 num_examples: 54 download_size: 18379 dataset_size: 18980 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713215686" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
redwoodresearch/wmdp-cyber-deduped	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: dev num_bytes: 1772 num_examples: 5 - name: split_0 num_bytes: 36846 num_examples: 125 - name: split_1 num_bytes: 39721 num_examples: 125 - name: split_2 num_bytes: 39199 num_examples: 125 - name: split_3 num_bytes: 37203 num_examples: 125 - name: split_4 num_bytes: 39729 num_examples: 125 download_size: 139755 dataset_size: 194470 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: split_0 path: data/split_0-* - split: split_1 path: data/split_1-* - split: split_2 path: data/split_2-* - split: split_3 path: data/split_3-* - split: split_4 path: data/split_4-* ---
polinaeterna/test_verifications_checksums	--- dataset_info: features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 160 num_examples: 1 - name: test num_bytes: 8 num_examples: 5 download_size: 2688 dataset_size: 240 --- # Dataset Card for "test_verifications_checksums" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gcx/new_	--- license: other ---
LLMao/2024_03_10_03_19_49_Archive	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: content dtype: string - name: text dtype: string splits: - name: train num_bytes: 5251835 num_examples: 1433 download_size: 855697 dataset_size: 5251835 configs: - config_name: default data_files: - split: train path: data/train-* ---
ninoscherrer/moralchoice	--- pretty_name: MoralChoice license: cc-by-4.0 language: - en size_categories: - 1K<n<10K --- # Dataset Card for MoralChoice - Homepage: Coming Soon - Paper: Coming soon - Repository: [https://github.com/ninodimontalcino/moralchoice](https://github.com/ninodimontalcino/moralchoice) - Point of Contact: [Nino Scherrer & Claudia Shi](mailto:nino.scherrer@gmail.com,claudia.j.shi@gmail.com?subject=[MoralChoice]) ### Dataset Summary MoralChoice is a survey dataset to evaluate the moral beliefs encoded in LLMs. The dataset consists of: - Survey Question Meta-Data: 1767 hypothetical moral scenarios where each scenario consists of a description / context and two potential actions - Low-Ambiguity Moral Scenarios (687 scenarios): One action is clearly preferred over the other. - High-Ambiguity Moral Scenarios (680 scenarios): Neither action is clearly preferred - Survey Question Templates: 3 hand-curated question templates - Survey Responses: Outputs from 28 open- and closed-sourced LLMs A statistical workflow for analyzing the survey responses can be found in the corresponding [paper](). 🚧 Important: 🚧 - Moral scenarios and question templates are already available. - Survey responses will be uploaded shortly! ### Languages MoralChoice is only available in English. ## Dataset Structure ### Data Fields #### Moral Scenarios (Survey Question Meta-Data) ``` - scenario_id unique scenario identifier - ambiguity level of ambiguity (low or high) - generation_type generation type (hand-written or generated) - context scenario description / contextualization - action 1 description of a potential action - action 2 description of a potential action - a1_{rule} {rule} violation label of action 1 - a2_{rule} {rule} violation label of action 2 ``` #### Survey Question Templates ``` - name name of question template (e.g., ab, repeat, compare) - question_header question instruction header text - question question template with placeholders ``` #### Survey Responses ``` - scenario_id unique scenario identifier - model_id model identifier (e.g., openai/gpt-4) - question_type question type (ab: A or B?, repeat: Repeat the preferred answer, compare: Do you prefer A over B? ) - question_ordering question ordering label (0: default order, 1: flipped order) - question_header question instruction header text - question_text question text - answer_raw raw answer of model - decision semantic answer of model (e.g., action1, action2, refusal, invalid) - eval_technique evaluation technique used - eval_top_p evaluation parameter - top_p - eval_temperature evaluation parameter - temperature - timestamp timestamp of model access ``` ## Dataset Creation ### Generation of Moral Scenarios The construction of MoralChoice follows a three-step procedure: - Scenario Generation: We generate seperately low- and high-ambiguity scenarios (i.e., the triple of scenario context, action 1 and action 2) guided by the 10 rules of Gert's common morality framework. - Low-Ambiguity Scenarios: Zero-Shot Prompting Setup based on OpenAI's gpt-4 - High-Ambiguity Scenarios: Stochastic Few-Shot Prompting Setup based on OpenAI's text-davinci-003 using a a set of 100 hand-written scenarios - Scenario Curation: We check the validity and grammar of each generated scenario manually and remove invalid scenarios. In addition, we assess lexical similarity between the generated scenarios and remove duplicates and overly-similar scenarios. - Auxiliarly Label Aquisition: We acquire auxiliary rule violation labels through SurgeAI for every scenario. For detailed information, we refer to the corresponding paper. ## Collection of LLM responses Across all models, we employ temperature-based sampling with `top-p=1.0`and `temperature=1.0`. For every specific question form (unique combination of scenario, question template, answer option ordering), we collect multiple samples (5 for low-ambiguity scenarios and 10 for high-ambiguity scenarios). The raw sequence of token outputs were mapped to semantic action (see the corresponding paper for exact details). ### Annotations To acquire high-quality annotations, we employ experienced annotators sourced through the data-labeling company [Surge AI](https://www.surgehq.ai/). ## Considerations for Using the Data - Limited Diversity in Scenarios (professions, contexts) - Limited Diversity in Question-Templates - Limited to English ### Dataset Curators - Nino Scherrer ([Website](https://ninodimontalcino.github.io/), [Mail](mailto:nino.scherrer@gmail.com?subject=[MoralChoice])) - Claudia Shi ([Website](https://www.claudiajshi.com/), [Mail](mailto:nino.scherrer@gmail.com?subject=[MoralChoice])) ### Citation ``` @misc{scherrer2023moralchoice, title={Evaluating the Moral Beliefs Encoded in LLMs}, author={Scherrer, Nino and Shi, Claudia, and Feder, Amir and Blei, David}, year={2023}, journal={arXiv:} } ```
Deepexi/glaive-function-calling-vicuna	--- license: cc-by-4.0 language: - en size_categories: - 10K<n<100K --- 数据集格式说明： [[glaiveai/glaive-function-calling · Datasets at Hugging Face](https://huggingface.co/datasets/glaiveai/glaive-function-calling)](glaiveai/glaive-function-calling) 的 SFT 格式我们高兴地宣布，数据集 "glaiveai/glaive-function-calling" 已经根据 SFT（Supervised Fine-Tuning）的需求进行了格式转换，以支持大型语言模型的训练。以下是有关这一新格式的简要说明： 1. 数据集概述: 数据集 "glaiveai/glaive-function-calling" 基于 CC-BY-4.0 协议发布，原始数据集包含标识符和对话信息，这些数据已被转换为适应 SFT 训练的结构。 2. 数据格式：转换后的数据集格式包含以下关键信息： - `id`: 整数类型的标识符，用于唯一标识每个数据样本。 - `conversations`: 一个数组，其中包含对话信息。每个对话可以由多个句子组成，以更好地呈现函数调用的上下文。 3. 数据集用途：转换后的数据集适用于 SFT 的训练，主要用途包括但不限于： - 函数调用理解：通过分析对话中的函数调用信息，让语言模型更好地理解函数之间的关系，从而提高其代码理解能力。 - 上下文感知性：对话信息能够为模型提供更丰富的上下文，使其更准确地推断和生成代码片段。 - 代码生成与推荐：基于对话中的函数调用上下文，模型可以更精确地生成代码，并提供更合适的函数建议。通过将数据集 "glaiveai/glaive-function-calling" 转换为 SFT 格式，我们旨在为大型语言模型的训练提供更适合sft的函数调用数据，以提升其代码理解和生成的性能。如有任何问题或需要进一步帮助，请随时联系我们。感谢您对函数调用数据集及其应用的兴趣与支持！
bz-arc13/sharegpt-chinese	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 201910458 num_examples: 30015 download_size: 101105044 dataset_size: 201910458 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_jondurbin__airoboros-34b-3.3	--- pretty_name: Evaluation run of jondurbin/airoboros-34b-3.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jondurbin/airoboros-34b-3.3](https://huggingface.co/jondurbin/airoboros-34b-3.3)\ \ 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_jondurbin__airoboros-34b-3.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-03T06:36:09.032058](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-34b-3.3/blob/main/results_2024-04-03T06-36-09.032058.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\": 0.7384979768935336,\n\ \ \"acc_stderr\": 0.028846149322558155,\n \"acc_norm\": 0.7486960506623671,\n\ \ \"acc_norm_stderr\": 0.02940148738177012,\n \"mc1\": 0.42472460220318237,\n\ \ \"mc1_stderr\": 0.017304000957167477,\n \"mc2\": 0.5958885847450273,\n\ \ \"mc2_stderr\": 0.015660972854187622\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042198,\n\ \ \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693247\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6443935471021709,\n\ \ \"acc_stderr\": 0.0047771835089498085,\n \"acc_norm\": 0.8444532961561442,\n\ \ \"acc_norm_stderr\": 0.0036168436913607657\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7185185185185186,\n\ \ \"acc_stderr\": 0.03885004245800253,\n \"acc_norm\": 0.7185185185185186,\n\ \ \"acc_norm_stderr\": 0.03885004245800253\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \ \ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.77,\n\ \ \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \ \ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8188679245283019,\n \"acc_stderr\": 0.023702963526757787,\n\ \ \"acc_norm\": 0.8188679245283019,\n \"acc_norm_stderr\": 0.023702963526757787\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.875,\n\ \ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \ \ \"acc_norm_stderr\": 0.02765610492929436\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\":\ \ 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267437,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267437\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\": 0.81,\n\ \ \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.81,\n \ \ \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.774468085106383,\n \"acc_stderr\": 0.027321078417387536,\n\ \ \"acc_norm\": 0.774468085106383,\n \"acc_norm_stderr\": 0.027321078417387536\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5701754385964912,\n\ \ \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.5701754385964912,\n\ \ \"acc_norm_stderr\": 0.04657047260594964\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\"\ : 0.6772486772486772,\n \"acc_stderr\": 0.024078943243597016,\n \"\ acc_norm\": 0.6772486772486772,\n \"acc_norm_stderr\": 0.024078943243597016\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\"\ : 0.9,\n \"acc_stderr\": 0.017066403719657258,\n \"acc_norm\": 0.9,\n\ \ \"acc_norm_stderr\": 0.017066403719657258\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.645320197044335,\n \"acc_stderr\": 0.0336612448905145,\n\ \ \"acc_norm\": 0.645320197044335,\n \"acc_norm_stderr\": 0.0336612448905145\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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-high_school_european_history\|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781657,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781657\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\ \ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7692307692307693,\n \"acc_stderr\": 0.021362027725222717,\n\ \ \"acc_norm\": 0.7692307692307693,\n \"acc_norm_stderr\": 0.021362027725222717\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.4111111111111111,\n \"acc_stderr\": 0.029999923508706675,\n \ \ \"acc_norm\": 0.4111111111111111,\n \"acc_norm_stderr\": 0.029999923508706675\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8319327731092437,\n \"acc_stderr\": 0.024289102115692275,\n\ \ \"acc_norm\": 0.8319327731092437,\n \"acc_norm_stderr\": 0.024289102115692275\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3973509933774834,\n \"acc_stderr\": 0.0399552400768168,\n \"acc_norm\"\ : 0.3973509933774834,\n \"acc_norm_stderr\": 0.0399552400768168\n },\n\ \ \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9100917431192661,\n\ \ \"acc_stderr\": 0.012264304540230435,\n \"acc_norm\": 0.9100917431192661,\n\ \ \"acc_norm_stderr\": 0.012264304540230435\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\"\ : {\n \"acc\": 0.6064814814814815,\n \"acc_stderr\": 0.03331747876370312,\n\ \ \"acc_norm\": 0.6064814814814815,\n \"acc_norm_stderr\": 0.03331747876370312\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9068627450980392,\n \"acc_stderr\": 0.020397853969427,\n \"acc_norm\"\ : 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969427\n },\n\ \ \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\":\ \ 0.9071729957805907,\n \"acc_stderr\": 0.018889750550956715,\n \"\ acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.018889750550956715\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951538,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951538\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622804,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622804\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.02923927267563275,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.02923927267563275\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.026845765054553855,\n\ \ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.026845765054553855\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.046161430750285455,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.046161430750285455\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.033932957297610096,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.033932957297610096\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132366,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132366\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8876117496807152,\n\ \ \"acc_stderr\": 0.011294541351216554,\n \"acc_norm\": 0.8876117496807152,\n\ \ \"acc_norm_stderr\": 0.011294541351216554\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8005780346820809,\n \"acc_stderr\": 0.021511900654252552,\n\ \ \"acc_norm\": 0.8005780346820809,\n \"acc_norm_stderr\": 0.021511900654252552\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.740782122905028,\n\ \ \"acc_stderr\": 0.014655780837497722,\n \"acc_norm\": 0.740782122905028,\n\ \ \"acc_norm_stderr\": 0.014655780837497722\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.02229285828456807,\n\ \ \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02229285828456807\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8038585209003215,\n\ \ \"acc_stderr\": 0.022552447780478033,\n \"acc_norm\": 0.8038585209003215,\n\ \ \"acc_norm_stderr\": 0.022552447780478033\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.019935086092149886,\n\ \ \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.019935086092149886\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6205673758865248,\n \"acc_stderr\": 0.0289473388516141,\n \ \ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.0289473388516141\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5697522816166884,\n\ \ \"acc_stderr\": 0.012645361435115228,\n \"acc_norm\": 0.5697522816166884,\n\ \ \"acc_norm_stderr\": 0.012645361435115228\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7904411764705882,\n \"acc_stderr\": 0.02472311040767708,\n\ \ \"acc_norm\": 0.7904411764705882,\n \"acc_norm_stderr\": 0.02472311040767708\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7990196078431373,\n \"acc_stderr\": 0.016211938889655574,\n \ \ \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.016211938889655574\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8244897959183674,\n \"acc_stderr\": 0.024352800722970018,\n\ \ \"acc_norm\": 0.8244897959183674,\n \"acc_norm_stderr\": 0.024352800722970018\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8805970149253731,\n\ \ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\ \ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.572289156626506,\n\ \ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\ \ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.026168221344662297,\n\ \ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.026168221344662297\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.42472460220318237,\n\ \ \"mc1_stderr\": 0.017304000957167477,\n \"mc2\": 0.5958885847450273,\n\ \ \"mc2_stderr\": 0.015660972854187622\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047986\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2971948445792267,\n \ \ \"acc_stderr\": 0.012588685966624182\n }\n}\n```" repo_url: https://huggingface.co/jondurbin/airoboros-34b-3.3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|arc:challenge\|25_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-03T06-36-09.032058.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|gsm8k\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hellaswag\|10_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-03T06-36-09.032058.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-03T06-36-09.032058.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '/details_harness\|winogrande\|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-03T06-36-09.032058.parquet' - config_name: results data_files: - split: 2024_04_03T06_36_09.032058 path: - results_2024-04-03T06-36-09.032058.parquet - split: latest path: - results_2024-04-03T06-36-09.032058.parquet --- # Dataset Card for Evaluation run of jondurbin/airoboros-34b-3.3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jondurbin/airoboros-34b-3.3](https://huggingface.co/jondurbin/airoboros-34b-3.3) 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_jondurbin__airoboros-34b-3.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-03T06:36:09.032058](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-34b-3.3/blob/main/results_2024-04-03T06-36-09.032058.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": 0.7384979768935336, "acc_stderr": 0.028846149322558155, "acc_norm": 0.7486960506623671, "acc_norm_stderr": 0.02940148738177012, "mc1": 0.42472460220318237, "mc1_stderr": 0.017304000957167477, "mc2": 0.5958885847450273, "mc2_stderr": 0.015660972854187622 }, "harness\|arc:challenge\|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042198, "acc_norm": 0.674061433447099, "acc_norm_stderr": 0.013697432466693247 }, "harness\|hellaswag\|10": { "acc": 0.6443935471021709, "acc_stderr": 0.0047771835089498085, "acc_norm": 0.8444532961561442, "acc_norm_stderr": 0.0036168436913607657 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7185185185185186, "acc_stderr": 0.03885004245800253, "acc_norm": 0.7185185185185186, "acc_norm_stderr": 0.03885004245800253 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8188679245283019, "acc_stderr": 0.023702963526757787, "acc_norm": 0.8188679245283019, "acc_norm_stderr": 0.023702963526757787 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.875, "acc_stderr": 0.02765610492929436, "acc_norm": 0.875, "acc_norm_stderr": 0.02765610492929436 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267437, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267437 }, "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": 0.81, "acc_stderr": 0.03942772444036624, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036624 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.774468085106383, "acc_stderr": 0.027321078417387536, "acc_norm": 0.774468085106383, "acc_norm_stderr": 0.027321078417387536 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5701754385964912, "acc_stderr": 0.04657047260594964, "acc_norm": 0.5701754385964912, "acc_norm_stderr": 0.04657047260594964 }, "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": 0.6772486772486772, "acc_stderr": 0.024078943243597016, "acc_norm": 0.6772486772486772, "acc_norm_stderr": 0.024078943243597016 }, "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": 0.9, "acc_stderr": 0.017066403719657258, "acc_norm": 0.9, "acc_norm_stderr": 0.017066403719657258 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.645320197044335, "acc_stderr": 0.0336612448905145, "acc_norm": 0.645320197044335, "acc_norm_stderr": 0.0336612448905145 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165044, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165044 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781657, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781657 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7692307692307693, "acc_stderr": 0.021362027725222717, "acc_norm": 0.7692307692307693, "acc_norm_stderr": 0.021362027725222717 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4111111111111111, "acc_stderr": 0.029999923508706675, "acc_norm": 0.4111111111111111, "acc_norm_stderr": 0.029999923508706675 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8319327731092437, "acc_stderr": 0.024289102115692275, "acc_norm": 0.8319327731092437, "acc_norm_stderr": 0.024289102115692275 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3973509933774834, "acc_stderr": 0.0399552400768168, "acc_norm": 0.3973509933774834, "acc_norm_stderr": 0.0399552400768168 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9100917431192661, "acc_stderr": 0.012264304540230435, "acc_norm": 0.9100917431192661, "acc_norm_stderr": 0.012264304540230435 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6064814814814815, "acc_stderr": 0.03331747876370312, "acc_norm": 0.6064814814814815, "acc_norm_stderr": 0.03331747876370312 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9068627450980392, "acc_stderr": 0.020397853969427, "acc_norm": 0.9068627450980392, "acc_norm_stderr": 0.020397853969427 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9071729957805907, "acc_stderr": 0.018889750550956715, "acc_norm": 0.9071729957805907, "acc_norm_stderr": 0.018889750550956715 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8549618320610687, "acc_stderr": 0.03088466108951538, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.03088466108951538 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622804, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622804 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8981481481481481, "acc_stderr": 0.02923927267563275, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.02923927267563275 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8650306748466258, "acc_stderr": 0.026845765054553855, "acc_norm": 0.8650306748466258, "acc_norm_stderr": 0.026845765054553855 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6160714285714286, "acc_stderr": 0.046161430750285455, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.046161430750285455 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8640776699029126, "acc_stderr": 0.033932957297610096, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.033932957297610096 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132366, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132366 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8876117496807152, "acc_stderr": 0.011294541351216554, "acc_norm": 0.8876117496807152, "acc_norm_stderr": 0.011294541351216554 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8005780346820809, "acc_stderr": 0.021511900654252552, "acc_norm": 0.8005780346820809, "acc_norm_stderr": 0.021511900654252552 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.740782122905028, "acc_stderr": 0.014655780837497722, "acc_norm": 0.740782122905028, "acc_norm_stderr": 0.014655780837497722 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8137254901960784, "acc_stderr": 0.02229285828456807, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.02229285828456807 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8038585209003215, "acc_stderr": 0.022552447780478033, "acc_norm": 0.8038585209003215, "acc_norm_stderr": 0.022552447780478033 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8487654320987654, "acc_stderr": 0.019935086092149886, "acc_norm": 0.8487654320987654, "acc_norm_stderr": 0.019935086092149886 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6205673758865248, "acc_stderr": 0.0289473388516141, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.0289473388516141 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5697522816166884, "acc_stderr": 0.012645361435115228, "acc_norm": 0.5697522816166884, "acc_norm_stderr": 0.012645361435115228 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7904411764705882, "acc_stderr": 0.02472311040767708, "acc_norm": 0.7904411764705882, "acc_norm_stderr": 0.02472311040767708 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7990196078431373, "acc_stderr": 0.016211938889655574, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.016211938889655574 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8244897959183674, "acc_stderr": 0.024352800722970018, "acc_norm": 0.8244897959183674, "acc_norm_stderr": 0.024352800722970018 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8654970760233918, "acc_stderr": 0.026168221344662297, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.026168221344662297 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.42472460220318237, "mc1_stderr": 0.017304000957167477, "mc2": 0.5958885847450273, "mc2_stderr": 0.015660972854187622 }, "harness\|winogrande\|5": { "acc": 0.8208366219415943, "acc_stderr": 0.010777949156047986 }, "harness\|gsm8k\|5": { "acc": 0.2971948445792267, "acc_stderr": 0.012588685966624182 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
distilled-from-one-sec-cv12/chunk_240	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1464626612 num_examples: 285391 download_size: 1496867773 dataset_size: 1464626612 --- # Dataset Card for "chunk_240" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Cerrisete07/Raimundo	--- license: other ---
pradeep239/plain_penBlackW_250_PdfS	--- license: mit dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 386873256.0 num_examples: 680 - name: validation num_bytes: 44601877.0 num_examples: 80 - name: test num_bytes: 22237112.0 num_examples: 41 download_size: 365880585 dataset_size: 453712245.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
aquillesdaamizade/pony-head	--- license: other ---
ekazuki/embedding-to-group	--- dataset_info: features: - name: group dtype: string - name: embedding sequence: float64 splits: - name: train num_bytes: 965694758 num_examples: 313251 download_size: 814149184 dataset_size: 965694758 configs: - config_name: default data_files: - split: train path: data/train-* ---
lprat/wiki_articles_data	--- dataset_info: features: - name: texts dtype: string - name: questions dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 42832878 num_examples: 10001 download_size: 4511591 dataset_size: 42832878 --- # Dataset Card for "wiki_articles_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
euclaise/MegaCoT	--- dataset_info: features: - name: rationale dtype: string - name: target dtype: string - name: source dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 189154865 num_examples: 629377 download_size: 90889666 dataset_size: 189154865 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for MegaCoT Slightly more detailed (to allow for RL) and extended re-creation of Flan's CoT submix, excluding ecqa (which contains synthetic generations). Extra datasets not in the original CoT collection: - [ROPES](https://huggingface.co/datasets/ropes) - [Entailmentbank](https://github.com/allenai/entailment_bank) (excluding task 2) - edited to include random unrelated context points in some examples - [MATH](https://huggingface.co/datasets/competition_math) (up to level 2) - [cos_e](https://huggingface.co/datasets/cos_e) - [feasibilityQA](https://huggingface.co/datasets/tasksource/feasibilityQA) - [TAL-SCQ5K](https://huggingface.co/datasets/math-eval/TAL-SCQ5K) - [Quartz](https://huggingface.co/datasets/quartz) - [boolq](https://huggingface.co/datasets/boolq) - [FLUTE](https://huggingface.co/datasets/ColumbiaNLP/FLUTE) I reserve no rights to the dataset, but the original datasets were made available under various public licenses. Hence, consider each subset of this dataset to be licensed as the original dataset from where it comes was.
carloscapote/es.wiktionary.org	--- license: cc-by-sa-4.0 language: - es pretty_name: Spanish Wiktionary size_categories: - 100K<n<1M --- # Spanish Wiktionary ## Motivation Multilingual datasets based in Wikimedia Foundation's Wiktionary tend to use its translation system to fetch non English words, what causes a lot of words and definitions being discarded. ## Development In order to solve this, I wrote a [custom parser](https://github.com/elcapo/eswiktionary_parser) that obtains the definitions straight from a dump of the Spanish Wiktionary. Both the parser and the dataset will be developed in harmony. ## Stage Both the parser and this dataset are in a very early stage of development but they already provide a list of 873.990 definitions that are easy to read and process for machine learning purposes. ## Contact Feel free to [contact me](https://github.com/elcapo) if you are interested in contributing with either the parser, or the dataset.
dar-tau/test-test	--- dataset_info: features: - name: logit_loss dtype: float64 - name: log_kl_loss dtype: float64 - name: mask_weight dtype: float64 - name: probe_loss dtype: float64 - name: top_k_acc dtype: float64 - name: attention_maps sequence: float32 - name: attention_maps_shape sequence: int64 splits: - name: train num_bytes: 83472 num_examples: 3 download_size: 11666 dataset_size: 83472 configs: - config_name: default data_files: - split: train path: data/train-* ---
BLACKBUN/paediatrics_abdominal_pain	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: Disease dtype: string - name: Explanation dtype: string splits: - name: train num_bytes: 393173 num_examples: 143 download_size: 165813 dataset_size: 393173 --- # Dataset Card for "paediatrics_abdominal_pain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Codec-SUPERB/mridangam_synth	--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: id dtype: string splits: - name: original num_bytes: 543097298.0 num_examples: 6977 - name: academicodec_hifi_16k_320d num_bytes: 179183122.103 num_examples: 6977 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 179183122.103 num_examples: 6977 - name: academicodec_hifi_24k_320d num_bytes: 268488722.103 num_examples: 6977 - name: audiodec_24k_320d num_bytes: 272675795.103 num_examples: 6977 - name: dac_16k num_bytes: 181415762.103 num_examples: 6977 - name: dac_24k num_bytes: 271837682.103 num_examples: 6977 - name: dac_44k num_bytes: 499022756.103 num_examples: 6977 - name: encodec_24k_12bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_1_5bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_24bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_3bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_6bps num_bytes: 271837682.103 num_examples: 6977 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 181415762.103 num_examples: 6977 - name: speech_tokenizer_16k num_bytes: 183648402.103 num_examples: 6977 download_size: 2585326580 dataset_size: 5026235644.957 --- # Dataset Card for "mridangam_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
torchgeo/ChesapeakeRSC	--- license: mit task_categories: - image-segmentation size_categories: - 10K<n<100K tags: - remote sensing - road segmentation ---
CyberHarem/clukay_neuralcloud	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of clukay/クルカイ/可露凯 (Neural Cloud) This is the dataset of clukay/クルカイ/可露凯 (Neural Cloud), containing 500 images and their tags. The core tags of this character are `long_hair, green_eyes, bangs, hair_ornament, facial_mark, breasts, grey_hair, hat, blunt_bangs, 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 \| 500 \| 816.53 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 416.28 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1299 \| 943.00 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 700.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1299 \| 1.38 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/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/clukay_neuralcloud', 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 \| 49 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, h&k_hk416, jacket, solo, assault_rifle, beret, holding_gun, white_gloves, looking_at_viewer, black_thighhighs, long_sleeves, black_headwear, pleated_skirt, heckler_&_koch, plaid_skirt, teardrop, medium_breasts, trigger_discipline, bag, blush, white_hair \| \| 1 \| 13 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_thighhighs, jacket, looking_at_viewer, solo, white_background, beret, black_headwear, long_sleeves, medium_breasts, pleated_skirt, simple_background, blush, white_gloves, plaid_skirt, closed_mouth, black_skirt, white_hair, zettai_ryouiki \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, jacket, looking_at_viewer, solo, upper_body, beret, white_background, blush, long_sleeves, medium_breasts, simple_background, white_gloves, black_headwear, closed_mouth, teardrop, white_hair, clothes_writing \| \| 3 \| 16 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bare_shoulders, black_gloves, official_alternate_costume, solo, strapless_dress, cleavage, collarbone, layered_dress, looking_at_viewer, white_hairband, black_thighhighs, blush, flower, large_breasts, closed_mouth, black_choker, earrings, hair_ribbon, purple_dress, smile, teardrop, simple_background, white_background, medium_breasts, multi-tied_hair, skirt_hold, bare_arms \| \| 4 \| 13 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, cleavage, official_alternate_costume, solo, bare_shoulders, detached_sleeves, rabbit_ears, white_dress, beret, large_breasts, looking_at_viewer, long_sleeves, strapless_dress, closed_mouth, hair_ribbon, puffy_sleeves, choker, mismatched_legwear, white_hair, pantyhose, simple_background, teardrop, white_background \| \| 5 \| 59 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, official_alternate_costume, solo, blush, mini_hat, long_sleeves, looking_at_viewer, black_apron, checkered_clothes, hair_ribbon, tilted_headwear, white_background, bowtie, black_cat, animal, white_dress, white_shirt, closed_mouth, simple_background, aged_down, frilled_apron, black_hairband, purple_headwear, black_bow, white_hair \| \| 6 \| 14 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, blush, cleavage, looking_at_viewer, solo, navel, collarbone, large_breasts, teardrop, bare_shoulders, stomach, sidelocks, blue_sky, closed_mouth, day, outdoors, halterneck, ocean, side-tie_bikini_bottom, thighs, beach, black_bikini, cloud, cowboy_shot, front-tie_bikini_top, medium_breasts, water, wet, white_background, jewelry, simple_background \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, closed_mouth, looking_at_viewer, simple_background, solo, white_background, blue_bow, braid, collared_shirt, plaid_skirt, pleated_skirt, school_uniform, white_shirt, blush, long_sleeves, medium_breasts, white_skirt, black_bow, black_jacket, blazer, hair_bow, pantyhose, school_bag, short_sleeves \| \| 8 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, cleavage, blush, looking_at_viewer, solo, navel, closed_mouth, collarbone, bare_shoulders, white_bra, large_breasts, medium_breasts, white_panties, cowboy_shot, long_sleeves, sitting, stomach, teardrop, white_background, bow, open_clothes, simple_background, thighhighs, underwear_only \| \| 9 \| 8 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, bare_shoulders, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, solo, blush, pantyhose, black_leotard, cleavage, large_breasts, wrist_cuffs, collarbone, detached_collar, hairband, simple_background, strapless_leotard, white_background, closed_mouth, rabbit_tail, teardrop, open_mouth \| \| 10 \| 5 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| 1girl, floral_print, looking_at_viewer, obi, solo, blush, holding, long_sleeves, alternate_hairstyle, blue_kimono, closed_mouth, hair_flower, print_kimono, sidelocks, smile, yukata, aerial_fireworks, bagged_fish, blue_flower, blurry_background, braid, from_side, goldfish, new_year, outdoors, purple_kimono, signature, single_hair_bun, sitting, upper_body, wide_sleeves \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| h&k_hk416 \| jacket \| solo \| assault_rifle \| beret \| holding_gun \| white_gloves \| looking_at_viewer \| black_thighhighs \| long_sleeves \| black_headwear \| pleated_skirt \| heckler_&_koch \| plaid_skirt \| teardrop \| medium_breasts \| trigger_discipline \| bag \| blush \| white_hair \| white_background \| simple_background \| closed_mouth \| black_skirt \| zettai_ryouiki \| upper_body \| clothes_writing \| bare_shoulders \| black_gloves \| official_alternate_costume \| strapless_dress \| cleavage \| collarbone \| layered_dress \| white_hairband \| flower \| large_breasts \| black_choker \| earrings \| hair_ribbon \| purple_dress \| smile \| multi-tied_hair \| skirt_hold \| bare_arms \| detached_sleeves \| rabbit_ears \| white_dress \| puffy_sleeves \| choker \| mismatched_legwear \| pantyhose \| mini_hat \| black_apron \| checkered_clothes \| tilted_headwear \| bowtie \| black_cat \| animal \| white_shirt \| aged_down \| frilled_apron \| black_hairband \| purple_headwear \| black_bow \| navel \| stomach \| sidelocks \| blue_sky \| day \| outdoors \| halterneck \| ocean \| side-tie_bikini_bottom \| thighs \| beach \| black_bikini \| cloud \| cowboy_shot \| front-tie_bikini_top \| water \| wet \| jewelry \| blue_bow \| braid \| collared_shirt \| school_uniform \| white_skirt \| black_jacket \| blazer \| hair_bow \| school_bag \| short_sleeves \| white_bra \| white_panties \| sitting \| bow \| open_clothes \| thighhighs \| underwear_only \| fake_animal_ears \| playboy_bunny \| black_leotard \| wrist_cuffs \| detached_collar \| hairband \| strapless_leotard \| rabbit_tail \| open_mouth \| floral_print \| obi \| holding \| alternate_hairstyle \| blue_kimono \| hair_flower \| print_kimono \| yukata \| aerial_fireworks \| bagged_fish \| blue_flower \| blurry_background \| from_side \| goldfish \| new_year \| purple_kimono \| signature \| single_hair_bun \| wide_sleeves \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:------------\|:---------\|:-------\|:----------------\|:--------\|:--------------\|:---------------\|:--------------------\|:-------------------\|:---------------\|:-----------------\|:----------------\|:-----------------\|:--------------\|:-----------\|:-----------------\|:---------------------\|:------\|:--------\|:-------------\|:-------------------\|:--------------------\|:---------------\|:--------------\|:-----------------\|:-------------\|:------------------\|:-----------------\|:---------------\|:-----------------------------\|:------------------\|:-----------\|:-------------\|:----------------\|:-----------------\|:---------\|:----------------\|:---------------\|:-----------\|:--------------\|:---------------\|:--------\|:------------------\|:-------------\|:------------\|:-------------------\|:--------------\|:--------------\|:----------------\|:---------\|:---------------------\|:------------\|:-----------\|:--------------\|:--------------------\|:------------------\|:---------\|:------------\|:---------\|:--------------\|:------------\|:----------------\|:-----------------\|:------------------\|:------------\|:--------\|:----------\|:------------\|:-----------\|:------\|:-----------\|:-------------\|:--------\|:-------------------------\|:---------\|:--------\|:---------------\|:--------\|:--------------\|:-----------------------\|:--------\|:------\|:----------\|:-----------\|:--------\|:-----------------\|:-----------------\|:--------------\|:---------------\|:---------\|:-----------\|:-------------\|:----------------\|:------------\|:----------------\|:----------\|:------\|:---------------\|:-------------\|:-----------------\|:-------------------\|:----------------\|:----------------\|:--------------\|:------------------\|:-----------\|:--------------------\|:--------------\|:-------------\|:---------------\|:------\|:----------\|:----------------------\|:--------------\|:--------------\|:---------------\|:---------\|:-------------------\|:--------------\|:--------------\|:--------------------\|:------------\|:-----------\|:-----------\|:----------------\|:------------\|:------------------\|:---------------\| \| 0 \| 49 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 13 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| X \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| X \| \| X \| \| X \| X \| \| X \| X \| \| \| \| X \| X \| \| \| X \| X \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 16 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| X \| \| \| \| \| X \| X \| \| \| \| \| \| X \| X \| \| \| X \| \| X \| X \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 13 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| X \| \| X \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| X \| \| X \| X \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 59 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 14 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| \| \| X \| \| X \| X \| X \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 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 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| X \| \| \| \| \| X \| \| X \| \| \| \| \| X \| X \| \| \| X \| \| X \| X \| X \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 8 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| X \| \| X \| X \| X \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 5 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| X \| \| \| X \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
reza-alipour/muse-landmark-1500	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: mask dtype: image - name: caption dtype: string - name: caption_fre dtype: string - name: caption_deu dtype: string - name: caption_ita dtype: string - name: caption_spa dtype: string - name: generated_mask dtype: image splits: - name: train num_bytes: 570015596.25 num_examples: 1498 download_size: 548973105 dataset_size: 570015596.25 configs: - config_name: default data_files: - split: train path: data/train-* ---
Leon-LLM/Leon-Chess-Dataset-270k-All-Moves-BOS	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 85112470 num_examples: 270052 download_size: 6507602 dataset_size: 85112470 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Leon-Chess-Dataset-270k-All-Moves-BOS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Brizape/Variome_ibo	--- dataset_info: features: - name: id dtype: string - name: token sequence: string - name: ner_tags sequence: int64 splits: - name: train num_bytes: 5447859.708333333 num_examples: 76 - name: validation num_bytes: 1433647.2916666667 num_examples: 20 - name: test num_bytes: 1220147 num_examples: 24 download_size: 691714 dataset_size: 8101654.0 --- # Dataset Card for "Variome_ibo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shidowake/FreedomIntelligence_alpaca-gpt4-japanese_subset_split_0	--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 4863217.322740098 num_examples: 4997 download_size: 2532014 dataset_size: 4863217.322740098 configs: - config_name: default data_files: - split: train path: data/train-* ---
adamjweintraut/loaf	--- dataset_info: features: - name: midi_id dtype: string - name: song_title dtype: string - name: lyrics dtype: string - name: genre dtype: string - name: lyric_summary_bart dtype: string - name: lyric_summary_bartv2 dtype: string - name: topic dtype: string - name: orig_syl_tokens dtype: string - name: orig_note_onsets_tokens dtype: string - name: orig_note_dur_tokens dtype: string - name: orig_note_pitch_tokens dtype: string - name: orig_note_vel_tokens dtype: string - name: orig_note_stress_tokens dtype: string - name: plan_orig dtype: string - name: lines sequence: string - name: kws_per_line sequence: sequence: string - name: plan_target dtype: string - name: lyrics_target dtype: string splits: - name: train num_bytes: 123531832 num_examples: 4489 - name: test num_bytes: 15590559 num_examples: 571 - name: valid num_bytes: 16578892 num_examples: 622 download_size: 36541760 dataset_size: 155701283 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
CyberHarem/wakana_shiki_lovelivesuperstar	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of wakana_shiki/若菜四季/와카나시키 (Love Live! Superstar!!) This is the dataset of wakana_shiki/若菜四季/와카나시키 (Love Live! Superstar!!), containing 239 images and their tags. The core tags of this character are `blue_hair, short_hair, bangs, hair_between_eyes, earrings, ribbon, breasts, red_ribbon, neck_ribbon, orange_eyes, red_eyes`, 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 \| 239 \| 310.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 239 \| 157.30 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 567 \| 353.54 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 239 \| 266.37 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 567 \| 544.16 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/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/wakana_shiki_lovelivesuperstar', 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 \| 31 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, yuigaoka_school_uniform, looking_at_viewer, white_shirt, pinafore_dress, blue_jacket, grey_dress, collared_shirt, long_sleeves, white_background, open_jacket, blush, simple_background, upper_body, jewelry, parted_lips \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 2girls, solo_focus, looking_at_viewer, smile, jacket, jewelry, upper_body, birthday, blonde_hair, blush, red_hair, yuigaoka_school_uniform \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, looking_at_viewer, navel, solo, blush, cleavage, collarbone, large_breasts, hair_flower, necklace, open_mouth, brown_eyes, side-tie_bikini_bottom, sitting, smile, thighs \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, looking_at_viewer, midriff, navel, tied_shirt, blue_shirt, blush, medium_breasts, short_sleeves, shorts, solo, arms_up, open_mouth, parted_lips, simple_background, stud_earrings, sweat, white_background, white_pants \| \| 4 \| 7 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1boy, 1girl, blush, hetero, penis, solo_focus, jewelry, navel, nipples, pov, pussy, sex, spread_legs, sweat, vaginal, completely_nude, crossed_bangs, looking_at_viewer, mosaic_censoring, open_mouth, dark-skinned_male, on_back, collarbone, large_breasts, missionary, motion_lines, stomach, upper_teeth_only \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| yuigaoka_school_uniform \| looking_at_viewer \| white_shirt \| pinafore_dress \| blue_jacket \| grey_dress \| collared_shirt \| long_sleeves \| white_background \| open_jacket \| blush \| simple_background \| upper_body \| jewelry \| parted_lips \| 2girls \| solo_focus \| smile \| jacket \| birthday \| blonde_hair \| red_hair \| navel \| cleavage \| collarbone \| large_breasts \| hair_flower \| necklace \| open_mouth \| brown_eyes \| side-tie_bikini_bottom \| sitting \| thighs \| midriff \| tied_shirt \| blue_shirt \| medium_breasts \| short_sleeves \| shorts \| arms_up \| stud_earrings \| sweat \| white_pants \| 1boy \| hetero \| penis \| nipples \| pov \| pussy \| sex \| spread_legs \| vaginal \| completely_nude \| crossed_bangs \| mosaic_censoring \| dark-skinned_male \| on_back \| missionary \| motion_lines \| stomach \| upper_teeth_only \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------------\|:--------------------\|:--------------\|:-----------------\|:--------------\|:-------------\|:-----------------\|:---------------\|:-------------------\|:--------------\|:--------\|:--------------------\|:-------------\|:----------\|:--------------\|:---------\|:-------------\|:--------\|:---------\|:-----------\|:--------------\|:-----------\|:--------\|:-----------\|:-------------\|:----------------\|:--------------\|:-----------\|:-------------\|:-------------\|:-------------------------\|:----------\|:---------\|:----------\|:-------------\|:-------------\|:-----------------\|:----------------\|:---------\|:----------\|:----------------\|:--------\|:--------------\|:-------\|:---------\|:--------\|:----------\|:------\|:--------\|:------\|:--------------\|:----------\|:------------------\|:----------------\|:-------------------\|:--------------------\|:----------\|:-------------\|:---------------\|:----------\|:-------------------\| \| 0 \| 31 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| X \| \| \| \| \| \| \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 7 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
RiverTest/Testmtg	--- license: apache-2.0 splits: - name: train description: The training dataset (train_Mtg_Dataset.parquet) is used to train the model. - name: test description: The test dataset (test_Mtg_Dataset.parquet) is used to evaluate the model performance on unseen data. ---
SwarmSageGuru/Cifar100	--- license: mit ---
zolak/twitter_dataset_78_1713076263	--- 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: 2661853 num_examples: 6509 download_size: 1344594 dataset_size: 2661853 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/beidou_genshin	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of beidou/北斗/北斗 (Genshin Impact) This is the dataset of beidou/北斗/北斗 (Genshin Impact), containing 500 images and their tags. The core tags of this character are `long_hair, eyepatch, breasts, red_eyes, brown_hair, hair_ornament, hair_over_one_eye, large_breasts, earrings`, 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 \| 932.49 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/beidou_genshin/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 784.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/beidou_genshin/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1284 \| 1.47 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/beidou_genshin/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/beidou_genshin', 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 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, fur_trim, hair_stick, hairpin, jewelry, solo, upper_body, chinese_clothes, one_eye_covered, smile, cleavage, dress, looking_at_viewer, black_gloves, fingerless_gloves, parted_lips, simple_background, white_background, capelet \| \| 1 \| 30 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_gloves, hairpin, solo, chinese_clothes, fingerless_gloves, hair_stick, fur_trim, one_eye_covered, looking_at_viewer, holding_sword, cleavage, smile, thighhighs, jewelry, red_dress, red_capelet, pelvic_curtain, vision_(genshin_impact), boots, greatsword \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, black_gloves, cleavage, dress, fur_trim, hair_stick, hairpin, looking_at_viewer, one_eye_covered, pelvic_curtain, smile, solo, fingerless_gloves, red_capelet, simple_background, thighs, white_background, chinese_clothes, jewelry, black_thighhighs, boots, vision_(genshin_impact) \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bare_shoulders, black_bikini, cleavage, looking_at_viewer, navel, solo, collarbone, hairpin, stomach, barefoot, blush, hair_stick, thighs, water, abs, cameltoe, feet, grin, jewelry, muscular_female, ocean, one_eye_covered, toned, wet, white_background \| \| 4 \| 17 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, hair_stick, hairpin, one_eye_covered, erection, jewelry, navel, futanari, large_penis, looking_at_viewer, nipples, testicles, outdoors, sweat, uncensored, blue_sky, teeth, thighs, veiny_penis, ejaculation, grin, huge_penis, completely_nude, day, huge_breasts, projectile_cum, black_hair, cloud, stomach, 1boy, black_thighhighs, solo_focus \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| fur_trim \| hair_stick \| hairpin \| jewelry \| solo \| upper_body \| chinese_clothes \| one_eye_covered \| smile \| cleavage \| dress \| looking_at_viewer \| black_gloves \| fingerless_gloves \| parted_lips \| simple_background \| white_background \| capelet \| holding_sword \| thighhighs \| red_dress \| red_capelet \| pelvic_curtain \| vision_(genshin_impact) \| boots \| greatsword \| thighs \| black_thighhighs \| bare_shoulders \| black_bikini \| navel \| collarbone \| stomach \| barefoot \| blush \| water \| abs \| cameltoe \| feet \| grin \| muscular_female \| ocean \| toned \| wet \| erection \| futanari \| large_penis \| nipples \| testicles \| outdoors \| sweat \| uncensored \| blue_sky \| teeth \| veiny_penis \| ejaculation \| huge_penis \| completely_nude \| day \| huge_breasts \| projectile_cum \| black_hair \| cloud \| 1boy \| solo_focus \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:-------------\|:----------\|:----------\|:-------\|:-------------\|:------------------\|:------------------\|:--------\|:-----------\|:--------\|:--------------------\|:---------------\|:--------------------\|:--------------\|:--------------------\|:-------------------\|:----------\|:----------------\|:-------------\|:------------\|:--------------\|:-----------------\|:--------------------------\|:--------\|:-------------\|:---------\|:-------------------\|:-----------------\|:---------------\|:--------\|:-------------\|:----------\|:-----------\|:--------\|:--------\|:------\|:-----------\|:-------\|:-------\|:------------------\|:--------\|:--------\|:------\|:-----------\|:-----------\|:--------------\|:----------\|:------------\|:-----------\|:--------\|:-------------\|:-----------\|:--------\|:--------------\|:--------------\|:-------------\|:------------------\|:------\|:---------------\|:-----------------\|:-------------\|:--------\|:-------\|:-------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 30 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| X \| X \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| \| \| \| X \| X \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 17 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| X \| X \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
jaiganesan/real_splitted_image	--- dataset_info: features: - name: image dtype: image - name: text sequence: string splits: - name: train num_bytes: 131840372.0 num_examples: 1000 download_size: 131699741 dataset_size: 131840372.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
pluseen/amore_231101	--- license: apache-2.0 --- <https://civitai.com/models/54867?modelVersionId=207286>
CyberHarem/onitsuka_natsumi_lovelivesuperstar	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of onitsuka_natsumi/鬼塚夏美 (Love Live! Superstar!!) This is the dataset of onitsuka_natsumi/鬼塚夏美 (Love Live! Superstar!!), containing 231 images and their tags. The core tags of this character are `blonde_hair, multicolored_hair, gradient_hair, pink_hair, long_hair, braid, twin_braids, pink_eyes, hair_ornament, hair_flower, bangs, red_eyes, 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 \| 231 \| 302.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 231 \| 155.76 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 552 \| 347.21 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 231 \| 262.00 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 552 \| 535.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/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/onitsuka_natsumi_lovelivesuperstar', 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 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, flower, looking_at_viewer, neck_ribbon, red_ribbon, solo, yuigaoka_school_uniform, blue_jacket, blush, grey_dress, long_sleeves, white_background, collared_shirt, grin, one_eye_closed, open_jacket, pinafore_dress, simple_background, white_shirt \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, red_ribbon, short_sleeves, solo, white_shirt, yuigaoka_school_uniform, neck_ribbon, pinafore_dress, upper_body, flower, summer_uniform, white_background, collared_shirt, blush, grin, one_eye_closed, simple_background \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, looking_at_viewer, solo, yuigaoka_school_uniform, flower, smile, dated, happy_birthday, blush, english_text, jacket, character_name, dress, pink_background, upper_body, signature \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, looking_at_viewer, sunglasses, bikini_skirt, cleavage, heart-shaped_eyewear, smile, solo, white_bikini, blush, bracelet, flower, large_breasts, navel, necklace, simple_background, white_background, eyewear_on_head, frills, pink-tinted_eyewear, tongue_out \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, hetero, nipples, solo_focus, large_breasts, pussy, sweat, open_mouth, spread_legs, vaginal, flower, 1boy, bar_censor, cum, navel, breast_grab, gangbang, grabbing, handjob, missionary, multiple_boys, multiple_penises, nude, on_back, panties \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| flower \| looking_at_viewer \| neck_ribbon \| red_ribbon \| solo \| yuigaoka_school_uniform \| blue_jacket \| blush \| grey_dress \| long_sleeves \| white_background \| collared_shirt \| grin \| one_eye_closed \| open_jacket \| pinafore_dress \| simple_background \| white_shirt \| short_sleeves \| upper_body \| summer_uniform \| smile \| dated \| happy_birthday \| english_text \| jacket \| character_name \| dress \| pink_background \| signature \| sunglasses \| bikini_skirt \| cleavage \| heart-shaped_eyewear \| white_bikini \| bracelet \| large_breasts \| navel \| necklace \| eyewear_on_head \| frills \| pink-tinted_eyewear \| tongue_out \| hetero \| nipples \| solo_focus \| pussy \| sweat \| open_mouth \| spread_legs \| vaginal \| 1boy \| bar_censor \| cum \| breast_grab \| gangbang \| grabbing \| handjob \| missionary \| multiple_boys \| multiple_penises \| nude \| on_back \| panties \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:--------------------\|:--------------\|:-------------\|:-------\|:--------------------------\|:--------------\|:--------\|:-------------\|:---------------\|:-------------------\|:-----------------\|:-------\|:-----------------\|:--------------\|:-----------------\|:--------------------\|:--------------\|:----------------\|:-------------\|:-----------------\|:--------\|:--------\|:-----------------\|:---------------\|:---------\|:-----------------\|:--------\|:------------------\|:------------\|:-------------\|:---------------\|:-----------\|:-----------------------\|:---------------\|:-----------\|:----------------\|:--------\|:-----------\|:------------------\|:---------\|:----------------------\|:-------------\|:---------\|:----------\|:-------------\|:--------\|:--------\|:-------------\|:--------------\|:----------\|:-------\|:-------------\|:------\|:--------------\|:-----------\|:-----------\|:----------\|:-------------\|:----------------\|:-------------------\|:-------\|:----------\|:----------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| \| \| X \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 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 \|
madrylab/imagenet-star	--- license: mit ---
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_112	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1314464156.0 num_examples: 258143 download_size: 1341360304 dataset_size: 1314464156.0 --- # Dataset Card for "chunk_112" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Aditya685/riddles_improved	--- dataset_info: features: - name: number dtype: int64 - name: messages sequence: string splits: - name: train num_bytes: 895763 num_examples: 669 download_size: 478793 dataset_size: 895763 configs: - config_name: default data_files: - split: train path: data/train-* ---
tr416/dataset_20231006_202418	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 762696.0 num_examples: 297 - name: test num_bytes: 7704.0 num_examples: 3 download_size: 73698 dataset_size: 770400.0 --- # Dataset Card for "dataset_20231006_202418" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BangumiBase/greatpretender	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Great Pretender This is the image base of bangumi Great Pretender, we detected 50 characters, 3820 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 178 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 20 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 647 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 471 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 86 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 79 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 80 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 100 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 110 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 116 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 48 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 35 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 58 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 23 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 30 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 61 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 33 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 59 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 132 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 24 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 9 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 44 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 25 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 42 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 25 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 25 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 83 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 14 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 265 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 16 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 16 \| [Download](30/dataset.zip) \| ![preview 1](30/preview_1.png) \| ![preview 2](30/preview_2.png) \| ![preview 3](30/preview_3.png) \| ![preview 4](30/preview_4.png) \| ![preview 5](30/preview_5.png) \| ![preview 6](30/preview_6.png) \| ![preview 7](30/preview_7.png) \| ![preview 8](30/preview_8.png) \| \| 31 \| 24 \| [Download](31/dataset.zip) \| ![preview 1](31/preview_1.png) \| ![preview 2](31/preview_2.png) \| ![preview 3](31/preview_3.png) \| ![preview 4](31/preview_4.png) \| ![preview 5](31/preview_5.png) \| ![preview 6](31/preview_6.png) \| ![preview 7](31/preview_7.png) \| ![preview 8](31/preview_8.png) \| \| 32 \| 16 \| [Download](32/dataset.zip) \| ![preview 1](32/preview_1.png) \| ![preview 2](32/preview_2.png) \| ![preview 3](32/preview_3.png) \| ![preview 4](32/preview_4.png) \| ![preview 5](32/preview_5.png) \| ![preview 6](32/preview_6.png) \| ![preview 7](32/preview_7.png) \| ![preview 8](32/preview_8.png) \| \| 33 \| 22 \| [Download](33/dataset.zip) \| ![preview 1](33/preview_1.png) \| ![preview 2](33/preview_2.png) \| ![preview 3](33/preview_3.png) \| ![preview 4](33/preview_4.png) \| ![preview 5](33/preview_5.png) \| ![preview 6](33/preview_6.png) \| ![preview 7](33/preview_7.png) \| ![preview 8](33/preview_8.png) \| \| 34 \| 38 \| [Download](34/dataset.zip) \| ![preview 1](34/preview_1.png) \| ![preview 2](34/preview_2.png) \| ![preview 3](34/preview_3.png) \| ![preview 4](34/preview_4.png) \| ![preview 5](34/preview_5.png) \| ![preview 6](34/preview_6.png) \| ![preview 7](34/preview_7.png) \| ![preview 8](34/preview_8.png) \| \| 35 \| 37 \| [Download](35/dataset.zip) \| ![preview 1](35/preview_1.png) \| ![preview 2](35/preview_2.png) \| ![preview 3](35/preview_3.png) \| ![preview 4](35/preview_4.png) \| ![preview 5](35/preview_5.png) \| ![preview 6](35/preview_6.png) \| ![preview 7](35/preview_7.png) \| ![preview 8](35/preview_8.png) \| \| 36 \| 7 \| [Download](36/dataset.zip) \| ![preview 1](36/preview_1.png) \| ![preview 2](36/preview_2.png) \| ![preview 3](36/preview_3.png) \| ![preview 4](36/preview_4.png) \| ![preview 5](36/preview_5.png) \| ![preview 6](36/preview_6.png) \| ![preview 7](36/preview_7.png) \| N/A \| \| 37 \| 18 \| [Download](37/dataset.zip) \| ![preview 1](37/preview_1.png) \| ![preview 2](37/preview_2.png) \| ![preview 3](37/preview_3.png) \| ![preview 4](37/preview_4.png) \| ![preview 5](37/preview_5.png) \| ![preview 6](37/preview_6.png) \| ![preview 7](37/preview_7.png) \| ![preview 8](37/preview_8.png) \| \| 38 \| 40 \| [Download](38/dataset.zip) \| ![preview 1](38/preview_1.png) \| ![preview 2](38/preview_2.png) \| ![preview 3](38/preview_3.png) \| ![preview 4](38/preview_4.png) \| ![preview 5](38/preview_5.png) \| ![preview 6](38/preview_6.png) \| ![preview 7](38/preview_7.png) \| ![preview 8](38/preview_8.png) \| \| 39 \| 15 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 2](39/preview_2.png) \| ![preview 3](39/preview_3.png) \| ![preview 4](39/preview_4.png) \| ![preview 5](39/preview_5.png) \| ![preview 6](39/preview_6.png) \| ![preview 7](39/preview_7.png) \| ![preview 8](39/preview_8.png) \| \| 40 \| 281 \| [Download](40/dataset.zip) \| ![preview 1](40/preview_1.png) \| ![preview 2](40/preview_2.png) \| ![preview 3](40/preview_3.png) \| ![preview 4](40/preview_4.png) \| ![preview 5](40/preview_5.png) \| ![preview 6](40/preview_6.png) \| ![preview 7](40/preview_7.png) \| ![preview 8](40/preview_8.png) \| \| 41 \| 21 \| [Download](41/dataset.zip) \| ![preview 1](41/preview_1.png) \| ![preview 2](41/preview_2.png) \| ![preview 3](41/preview_3.png) \| ![preview 4](41/preview_4.png) \| ![preview 5](41/preview_5.png) \| ![preview 6](41/preview_6.png) \| ![preview 7](41/preview_7.png) \| ![preview 8](41/preview_8.png) \| \| 42 \| 8 \| [Download](42/dataset.zip) \| ![preview 1](42/preview_1.png) \| ![preview 2](42/preview_2.png) \| ![preview 3](42/preview_3.png) \| ![preview 4](42/preview_4.png) \| ![preview 5](42/preview_5.png) \| ![preview 6](42/preview_6.png) \| ![preview 7](42/preview_7.png) \| ![preview 8](42/preview_8.png) \| \| 43 \| 11 \| [Download](43/dataset.zip) \| ![preview 1](43/preview_1.png) \| ![preview 2](43/preview_2.png) \| ![preview 3](43/preview_3.png) \| ![preview 4](43/preview_4.png) \| ![preview 5](43/preview_5.png) \| ![preview 6](43/preview_6.png) \| ![preview 7](43/preview_7.png) \| ![preview 8](43/preview_8.png) \| \| 44 \| 10 \| [Download](44/dataset.zip) \| ![preview 1](44/preview_1.png) \| ![preview 2](44/preview_2.png) \| ![preview 3](44/preview_3.png) \| ![preview 4](44/preview_4.png) \| ![preview 5](44/preview_5.png) \| ![preview 6](44/preview_6.png) \| ![preview 7](44/preview_7.png) \| ![preview 8](44/preview_8.png) \| \| 45 \| 18 \| [Download](45/dataset.zip) \| ![preview 1](45/preview_1.png) \| ![preview 2](45/preview_2.png) \| ![preview 3](45/preview_3.png) \| ![preview 4](45/preview_4.png) \| ![preview 5](45/preview_5.png) \| ![preview 6](45/preview_6.png) \| ![preview 7](45/preview_7.png) \| ![preview 8](45/preview_8.png) \| \| 46 \| 98 \| [Download](46/dataset.zip) \| ![preview 1](46/preview_1.png) \| ![preview 2](46/preview_2.png) \| ![preview 3](46/preview_3.png) \| ![preview 4](46/preview_4.png) \| ![preview 5](46/preview_5.png) \| ![preview 6](46/preview_6.png) \| ![preview 7](46/preview_7.png) \| ![preview 8](46/preview_8.png) \| \| 47 \| 76 \| [Download](47/dataset.zip) \| ![preview 1](47/preview_1.png) \| ![preview 2](47/preview_2.png) \| ![preview 3](47/preview_3.png) \| ![preview 4](47/preview_4.png) \| ![preview 5](47/preview_5.png) \| ![preview 6](47/preview_6.png) \| ![preview 7](47/preview_7.png) \| ![preview 8](47/preview_8.png) \| \| 48 \| 8 \| [Download](48/dataset.zip) \| ![preview 1](48/preview_1.png) \| ![preview 2](48/preview_2.png) \| ![preview 3](48/preview_3.png) \| ![preview 4](48/preview_4.png) \| ![preview 5](48/preview_5.png) \| ![preview 6](48/preview_6.png) \| ![preview 7](48/preview_7.png) \| ![preview 8](48/preview_8.png) \| \| noise \| 118 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
jaimin/Image	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1055814.0 num_examples: 3 download_size: 1058157 dataset_size: 1055814.0 --- # Dataset Card for "Image" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-squad-plain_text-47f200-2254171821

--- type: predictions tags: - autotrain - evaluation datasets: - squad eval_info: task: extractive_question_answering model: Palak/google_electra-small-discriminator_squad metrics: [] dataset_name: squad dataset_config: plain_text dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: Palak/google_electra-small-discriminator_squad * Dataset: squad * Config: plain_text * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Biryukovlex](https://huggingface.co/Biryukovlex) for evaluating this model.

heliosprime/twitter_dataset_1713147118

--- 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: 4395 num_examples: 12 download_size: 8734 dataset_size: 4395 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713147118" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_mtgv__MobileLLaMA-1.4B-Base

--- pretty_name: Evaluation run of mtgv/MobileLLaMA-1.4B-Base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mtgv/MobileLLaMA-1.4B-Base](https://huggingface.co/mtgv/MobileLLaMA-1.4B-Base)\ \ 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_mtgv__MobileLLaMA-1.4B-Base\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-06T23:22:22.302402](https://huggingface.co/datasets/open-llm-leaderboard/details_mtgv__MobileLLaMA-1.4B-Base/blob/main/results_2024-04-06T23-22-22.302402.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\": 0.2537476149311213,\n\ \ \"acc_stderr\": 0.03071237430286461,\n \"acc_norm\": 0.2549089996364952,\n\ \ \"acc_norm_stderr\": 0.03147631325439452,\n \"mc1\": 0.21664626682986537,\n\ \ \"mc1_stderr\": 0.014421468452506985,\n \"mc2\": 0.3481107244797803,\n\ \ \"mc2_stderr\": 0.013684586182211824\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.310580204778157,\n \"acc_stderr\": 0.013522292098053054,\n\ \ \"acc_norm\": 0.3438566552901024,\n \"acc_norm_stderr\": 0.013880644570156215\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.42949611631149176,\n\ \ \"acc_stderr\": 0.004939925958728871,\n \"acc_norm\": 0.5629356701852221,\n\ \ \"acc_norm_stderr\": 0.004950095555964667\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.26666666666666666,\n\ \ \"acc_stderr\": 0.03820169914517905,\n \"acc_norm\": 0.26666666666666666,\n\ \ \"acc_norm_stderr\": 0.03820169914517905\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.18421052631578946,\n \"acc_stderr\": 0.0315469804508223,\n\ \ \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.28,\n\ \ \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.22264150943396227,\n \"acc_stderr\": 0.025604233470899098,\n\ \ \"acc_norm\": 0.22264150943396227,\n \"acc_norm_stderr\": 0.025604233470899098\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.24305555555555555,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.16,\n \"acc_stderr\": 0.0368452949177471,\n \"acc_norm\"\ : 0.16,\n \"acc_norm_stderr\": 0.0368452949177471\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\ \ \"acc_stderr\": 0.030952890217749888,\n \"acc_norm\": 0.20809248554913296,\n\ \ \"acc_norm_stderr\": 0.030952890217749888\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.31063829787234043,\n \"acc_stderr\": 0.03025123757921317,\n\ \ \"acc_norm\": 0.31063829787234043,\n \"acc_norm_stderr\": 0.03025123757921317\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.03999423879281336,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.03999423879281336\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131184,\n\ \ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131184\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2857142857142857,\n \"acc_stderr\": 0.02326651221373057,\n \"\ acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.02326651221373057\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\ \ \"acc_stderr\": 0.04073524322147125,\n \"acc_norm\": 0.29365079365079366,\n\ \ \"acc_norm_stderr\": 0.04073524322147125\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\"\ : 0.26129032258064516,\n \"acc_stderr\": 0.024993053397764815,\n \"\ acc_norm\": 0.26129032258064516,\n \"acc_norm_stderr\": 0.024993053397764815\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.24630541871921183,\n \"acc_stderr\": 0.03031509928561773,\n \"\ acc_norm\": 0.24630541871921183,\n \"acc_norm_stderr\": 0.03031509928561773\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.23636363636363636,\n \"acc_stderr\": 0.03317505930009181,\n\ \ \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.03317505930009181\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.21717171717171718,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.21717171717171718,\n \"acc_norm_stderr\": 0.029376616484945633\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.20725388601036268,\n \"acc_stderr\": 0.029252823291803613,\n\ \ \"acc_norm\": 0.20725388601036268,\n \"acc_norm_stderr\": 0.029252823291803613\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.21025641025641026,\n \"acc_stderr\": 0.020660597485026924,\n\ \ \"acc_norm\": 0.21025641025641026,\n \"acc_norm_stderr\": 0.020660597485026924\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.22962962962962963,\n \"acc_stderr\": 0.025644108639267624,\n \ \ \"acc_norm\": 0.22962962962962963,\n \"acc_norm_stderr\": 0.025644108639267624\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.19747899159663865,\n \"acc_stderr\": 0.02585916412205146,\n\ \ \"acc_norm\": 0.19747899159663865,\n \"acc_norm_stderr\": 0.02585916412205146\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.24503311258278146,\n \"acc_stderr\": 0.03511807571804723,\n \"\ acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.03511807571804723\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.22568807339449543,\n \"acc_stderr\": 0.01792308766780305,\n \"\ acc_norm\": 0.22568807339449543,\n \"acc_norm_stderr\": 0.01792308766780305\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.23148148148148148,\n \"acc_stderr\": 0.028765111718046948,\n \"\ acc_norm\": 0.23148148148148148,\n \"acc_norm_stderr\": 0.028765111718046948\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604257,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604257\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658342,\n \ \ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.34080717488789236,\n\ \ \"acc_stderr\": 0.031811497470553604,\n \"acc_norm\": 0.34080717488789236,\n\ \ \"acc_norm_stderr\": 0.031811497470553604\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.03768335959728744,\n\ \ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.03768335959728744\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2644628099173554,\n \"acc_stderr\": 0.04026187527591207,\n \"\ acc_norm\": 0.2644628099173554,\n \"acc_norm_stderr\": 0.04026187527591207\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2767857142857143,\n\ \ \"acc_stderr\": 0.042466243366976256,\n \"acc_norm\": 0.2767857142857143,\n\ \ \"acc_norm_stderr\": 0.042466243366976256\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n\ \ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.02934311479809447,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.02934311479809447\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2796934865900383,\n\ \ \"acc_stderr\": 0.016050792148036553,\n \"acc_norm\": 0.2796934865900383,\n\ \ \"acc_norm_stderr\": 0.016050792148036553\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.26011560693641617,\n \"acc_stderr\": 0.023618678310069367,\n\ \ \"acc_norm\": 0.26011560693641617,\n \"acc_norm_stderr\": 0.023618678310069367\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.20915032679738563,\n \"acc_stderr\": 0.023287685312334806,\n\ \ \"acc_norm\": 0.20915032679738563,\n \"acc_norm_stderr\": 0.023287685312334806\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24437299035369775,\n\ \ \"acc_stderr\": 0.024406162094668886,\n \"acc_norm\": 0.24437299035369775,\n\ \ \"acc_norm_stderr\": 0.024406162094668886\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.2553191489361702,\n \"acc_stderr\": 0.026011992930901996,\n \"\ acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.026011992930901996\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2405475880052151,\n\ \ \"acc_stderr\": 0.010916406735478949,\n \"acc_norm\": 0.2405475880052151,\n\ \ \"acc_norm_stderr\": 0.010916406735478949\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.37272727272727274,\n \"acc_stderr\": 0.04631381319425463,\n\ \ \"acc_norm\": 0.37272727272727274,\n \"acc_norm_stderr\": 0.04631381319425463\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.1836734693877551,\n\ \ \"acc_stderr\": 0.02478907133200765,\n \"acc_norm\": 0.1836734693877551,\n\ \ \"acc_norm_stderr\": 0.02478907133200765\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.21393034825870647,\n \"acc_stderr\": 0.028996909693328906,\n\ \ \"acc_norm\": 0.21393034825870647,\n \"acc_norm_stderr\": 0.028996909693328906\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n\ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370519,\n\ \ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370519\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.29239766081871343,\n\ \ \"acc_stderr\": 0.03488647713457922,\n \"acc_norm\": 0.29239766081871343,\n\ \ \"acc_norm_stderr\": 0.03488647713457922\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.21664626682986537,\n \"mc1_stderr\": 0.014421468452506985,\n\ \ \"mc2\": 0.3481107244797803,\n \"mc2_stderr\": 0.013684586182211824\n\ \ },\n \"harness|winogrande|5\": {\n \"acc\": 0.5943172849250198,\n\ \ \"acc_stderr\": 0.013800206336014207\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.011372251705837756,\n \"acc_stderr\": 0.0029206661987887465\n\ \ }\n}\n```" repo_url: https://huggingface.co/mtgv/MobileLLaMA-1.4B-Base leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|arc:challenge|25_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-06T23-22-22.302402.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|gsm8k|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hellaswag|10_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-06T23-22-22.302402.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-management|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T23-22-22.302402.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|truthfulqa:mc|0_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-06T23-22-22.302402.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_06T23_22_22.302402 path: - '**/details_harness|winogrande|5_2024-04-06T23-22-22.302402.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-06T23-22-22.302402.parquet' - config_name: results data_files: - split: 2024_04_06T23_22_22.302402 path: - results_2024-04-06T23-22-22.302402.parquet - split: latest path: - results_2024-04-06T23-22-22.302402.parquet --- # Dataset Card for Evaluation run of mtgv/MobileLLaMA-1.4B-Base  Dataset automatically created during the evaluation run of model [mtgv/MobileLLaMA-1.4B-Base](https://huggingface.co/mtgv/MobileLLaMA-1.4B-Base) 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_mtgv__MobileLLaMA-1.4B-Base", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-06T23:22:22.302402](https://huggingface.co/datasets/open-llm-leaderboard/details_mtgv__MobileLLaMA-1.4B-Base/blob/main/results_2024-04-06T23-22-22.302402.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": 0.2537476149311213, "acc_stderr": 0.03071237430286461, "acc_norm": 0.2549089996364952, "acc_norm_stderr": 0.03147631325439452, "mc1": 0.21664626682986537, "mc1_stderr": 0.014421468452506985, "mc2": 0.3481107244797803, "mc2_stderr": 0.013684586182211824 }, "harness|arc:challenge|25": { "acc": 0.310580204778157, "acc_stderr": 0.013522292098053054, "acc_norm": 0.3438566552901024, "acc_norm_stderr": 0.013880644570156215 }, "harness|hellaswag|10": { "acc": 0.42949611631149176, "acc_stderr": 0.004939925958728871, "acc_norm": 0.5629356701852221, "acc_norm_stderr": 0.004950095555964667 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.24, "acc_stderr": 0.04292346959909284, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.26666666666666666, "acc_stderr": 0.03820169914517905, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.03820169914517905 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.18421052631578946, "acc_stderr": 0.0315469804508223, "acc_norm": 0.18421052631578946, "acc_norm_stderr": 0.0315469804508223 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.22264150943396227, "acc_stderr": 0.025604233470899098, "acc_norm": 0.22264150943396227, "acc_norm_stderr": 0.025604233470899098 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.24305555555555555, "acc_stderr": 0.0358687928008034, "acc_norm": 0.24305555555555555, "acc_norm_stderr": 0.0358687928008034 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.16, "acc_stderr": 0.0368452949177471, "acc_norm": 0.16, "acc_norm_stderr": 0.0368452949177471 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.26, "acc_stderr": 0.044084400227680794, "acc_norm": 0.26, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749888, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749888 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.31063829787234043, "acc_stderr": 0.03025123757921317, "acc_norm": 0.31063829787234043, "acc_norm_stderr": 0.03025123757921317 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.03999423879281336, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.03999423879281336 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.22758620689655173, "acc_stderr": 0.03493950380131184, "acc_norm": 0.22758620689655173, "acc_norm_stderr": 0.03493950380131184 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2857142857142857, "acc_stderr": 0.02326651221373057, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.02326651221373057 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147125, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147125 }, "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": 0.26129032258064516, "acc_stderr": 0.024993053397764815, "acc_norm": 0.26129032258064516, "acc_norm_stderr": 0.024993053397764815 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.24630541871921183, "acc_stderr": 0.03031509928561773, "acc_norm": 0.24630541871921183, "acc_norm_stderr": 0.03031509928561773 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.23636363636363636, "acc_stderr": 0.03317505930009181, "acc_norm": 0.23636363636363636, "acc_norm_stderr": 0.03317505930009181 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.21717171717171718, "acc_stderr": 0.029376616484945633, "acc_norm": 0.21717171717171718, "acc_norm_stderr": 0.029376616484945633 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.20725388601036268, "acc_stderr": 0.029252823291803613, "acc_norm": 0.20725388601036268, "acc_norm_stderr": 0.029252823291803613 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.21025641025641026, "acc_stderr": 0.020660597485026924, "acc_norm": 0.21025641025641026, "acc_norm_stderr": 0.020660597485026924 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.22962962962962963, "acc_stderr": 0.025644108639267624, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.025644108639267624 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.19747899159663865, "acc_stderr": 0.02585916412205146, "acc_norm": 0.19747899159663865, "acc_norm_stderr": 0.02585916412205146 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.24503311258278146, "acc_stderr": 0.03511807571804723, "acc_norm": 0.24503311258278146, "acc_norm_stderr": 0.03511807571804723 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22568807339449543, "acc_stderr": 0.01792308766780305, "acc_norm": 0.22568807339449543, "acc_norm_stderr": 0.01792308766780305 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.23148148148148148, "acc_stderr": 0.028765111718046948, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.028765111718046948 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604257, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604257 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.26582278481012656, "acc_stderr": 0.028756799629658342, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.028756799629658342 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.34080717488789236, "acc_stderr": 0.031811497470553604, "acc_norm": 0.34080717488789236, "acc_norm_stderr": 0.031811497470553604 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.24427480916030533, "acc_stderr": 0.03768335959728744, "acc_norm": 0.24427480916030533, "acc_norm_stderr": 0.03768335959728744 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2644628099173554, "acc_stderr": 0.04026187527591207, "acc_norm": 0.2644628099173554, "acc_norm_stderr": 0.04026187527591207 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.2962962962962963, "acc_stderr": 0.044143436668549335, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.044143436668549335 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.24539877300613497, "acc_stderr": 0.03380939813943354, "acc_norm": 0.24539877300613497, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.2767857142857143, "acc_stderr": 0.042466243366976256, "acc_norm": 0.2767857142857143, "acc_norm_stderr": 0.042466243366976256 }, "harness|hendrycksTest-management|5": { "acc": 0.2524271844660194, "acc_stderr": 0.04301250399690877, "acc_norm": 0.2524271844660194, "acc_norm_stderr": 0.04301250399690877 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02934311479809447, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02934311479809447 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.27, "acc_stderr": 0.04461960433384741, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384741 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2796934865900383, "acc_stderr": 0.016050792148036553, "acc_norm": 0.2796934865900383, "acc_norm_stderr": 0.016050792148036553 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.26011560693641617, "acc_stderr": 0.023618678310069367, "acc_norm": 0.26011560693641617, "acc_norm_stderr": 0.023618678310069367 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.20915032679738563, "acc_stderr": 0.023287685312334806, "acc_norm": 0.20915032679738563, "acc_norm_stderr": 0.023287685312334806 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.24437299035369775, "acc_stderr": 0.024406162094668886, "acc_norm": 0.24437299035369775, "acc_norm_stderr": 0.024406162094668886 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.25, "acc_stderr": 0.02409347123262133, "acc_norm": 0.25, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2553191489361702, "acc_stderr": 0.026011992930901996, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.026011992930901996 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2405475880052151, "acc_stderr": 0.010916406735478949, "acc_norm": 0.2405475880052151, "acc_norm_stderr": 0.010916406735478949 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.18382352941176472, "acc_stderr": 0.023529242185193106, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.023529242185193106 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.37272727272727274, "acc_stderr": 0.04631381319425463, "acc_norm": 0.37272727272727274, "acc_norm_stderr": 0.04631381319425463 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.1836734693877551, "acc_stderr": 0.02478907133200765, "acc_norm": 0.1836734693877551, "acc_norm_stderr": 0.02478907133200765 }, "harness|hendrycksTest-sociology|5": { "acc": 0.21393034825870647, "acc_stderr": 0.028996909693328906, "acc_norm": 0.21393034825870647, "acc_norm_stderr": 0.028996909693328906 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370519, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370519 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.29239766081871343, "acc_stderr": 0.03488647713457922, "acc_norm": 0.29239766081871343, "acc_norm_stderr": 0.03488647713457922 }, "harness|truthfulqa:mc|0": { "mc1": 0.21664626682986537, "mc1_stderr": 0.014421468452506985, "mc2": 0.3481107244797803, "mc2_stderr": 0.013684586182211824 }, "harness|winogrande|5": { "acc": 0.5943172849250198, "acc_stderr": 0.013800206336014207 }, "harness|gsm8k|5": { "acc": 0.011372251705837756, "acc_stderr": 0.0029206661987887465 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

jjuarez/autotrain-data-fine-tune

--- task_categories: - image-classification --- # AutoTrain Dataset for project: fine-tune ## Dataset Description This dataset has been automatically processed by AutoTrain for project fine-tune. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<382x256 RGB PIL image>", "target": 17 }, { "image": "<341x256 RGB PIL image>", "target": 7 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['apple', 'banana', 'cake', 'candy', 'carrot', 'cookie', 'doughnut', 'grape', 'hot dog', 'ice cream', 'juice', 'muffin', 'orange', 'pineapple', 'popcorn', 'pretzel', 'salad', 'strawberry', 'waffle', 'watermelon'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 5394 | | valid | 1351 |

makeouthill0071/xxxtentacion

--- license: openrail ---

Gabriel1322/joao

--- license: openrail ---

alexthomas4/highsub-classified-images

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Abullah '1': Adolf '2': Anya '3': Asta '4': Atom '5': Becky '6': Bond '7': Brando '8': Damian '9': Duncan '10': Epsilon '11': Franky '12': Gauche '13': Gesicht '14': Heracles '15': Kazuki '16': Kyutaro '17': Loid '18': Mimosa '19': Miri '20': Misaki '21': Nightfall '22': 'North #2' '23': Ochanomizu '24': Pluto '25': Rei '26': Side Character '27': Taiga '28': Tenma '29': Uran '30': Yor '31': Yuno '32': Yuri splits: - name: train num_bytes: 54171170.0 num_examples: 410 download_size: 54191470 dataset_size: 54171170.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

deetsadi/processed_dwi_sobel_all_b_values_large_mask_with_lesion

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: conditioning_image dtype: image splits: - name: train num_bytes: 41930844.0 num_examples: 200 download_size: 41926762 dataset_size: 41930844.0 --- # Dataset Card for "processed_dwi_sobel_all_b_values_large_mask_with_lesion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/firewatch_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of firewatch/ファイヤーウォッチ/守林人 (Arknights) This is the dataset of firewatch/ファイヤーウォッチ/守林人 (Arknights), containing 154 images and their tags. The core tags of this character are `horns, long_hair, grey_hair, sidelocks, ponytail, green_eyes, 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 | 154 | 275.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/firewatch_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 154 | 231.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/firewatch_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 390 | 447.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/firewatch_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/firewatch_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 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, long_sleeves, solo, open_jacket, black_gloves, black_skirt, closed_mouth, crossbow, green_jacket, looking_at_viewer, pleated_skirt, reindeer_antlers, outdoors, animal_ears, black_footwear, holding_gun, standing, green_pantyhose, collared_shirt, full_body, rifle, shoes, brown_eyes | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, antlers, green_jacket, long_sleeves, looking_at_viewer, open_jacket, solo, black_gloves, black_skirt, simple_background, white_background, backpack, collared_shirt, hand_up, pleated_skirt, brown_eyes, grey_eyes, official_alternate_costume, pantyhose, parted_lips, speech_bubble, sweater, upper_body | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, closed_mouth, solo, upper_body, open_jacket, collared_shirt, simple_background, animal_ears, reindeer_antlers, smile, hood_down, long_sleeves, looking_at_viewer, white_background | | 3 | 40 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, antlers, solo, bare_shoulders, looking_at_viewer, black_gloves, sleeveless_dress, white_dress, black_thighhighs, hairband, simple_background, white_background, bare_arms, yellow_eyes, closed_mouth, crossbow, bag | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | open_jacket | black_gloves | black_skirt | closed_mouth | crossbow | green_jacket | looking_at_viewer | pleated_skirt | reindeer_antlers | outdoors | animal_ears | black_footwear | holding_gun | standing | green_pantyhose | collared_shirt | full_body | rifle | shoes | brown_eyes | antlers | simple_background | white_background | backpack | hand_up | grey_eyes | official_alternate_costume | pantyhose | parted_lips | speech_bubble | sweater | upper_body | smile | hood_down | bare_shoulders | sleeveless_dress | white_dress | black_thighhighs | hairband | bare_arms | yellow_eyes | bag | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:--------------|:---------------|:--------------|:---------------|:-----------|:---------------|:--------------------|:----------------|:-------------------|:-----------|:--------------|:-----------------|:--------------|:-----------|:------------------|:-----------------|:------------|:--------|:--------|:-------------|:----------|:--------------------|:-------------------|:-----------|:----------|:------------|:-----------------------------|:------------|:--------------|:----------------|:----------|:-------------|:--------|:------------|:-----------------|:-------------------|:--------------|:-------------------|:-----------|:------------|:--------------|:------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | | X | X | X | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | | X | | | X | | X | | X | | | | | X | | | | | | X | X | | | | | | | | | X | X | X | | | | | | | | | | 3 | 40 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | | X | | X | X | | X | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X |

ai4bharat/IndicQuestionGeneration

--- annotations_creators: - no-annotation language_creators: - found language: - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te license: - cc-by-nc-4.0 multilinguality: - multilingual pretty_name: IndicQuestionGeneration size_categories: - 98K<n<98K source_datasets: - we start with the SQuAD question answering dataset repurposed to serve as a question generation dataset. We translate this dataset into different Indic languages. task_categories: - conditional-text-generation task_ids: - conditional-text-generation-other-question-generation --- # Dataset Card for "IndicQuestionGeneration" ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://indicnlp.ai4bharat.org/indicnlg-suite - **Paper:** [IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://arxiv.org/abs/2203.05437) - **Point of Contact:** ### Dataset Summary IndicQuestionGeneration is the question generation dataset released as part of IndicNLG Suite. Each example has five fields: id, squad_id, answer, context and question. We create this dataset in eleven languages, including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. This is translated data. The examples in each language are exactly similar but in different languages. The number of examples in each language is 98,027. ### Supported Tasks and Leaderboards **Tasks:** Question Generation **Leaderboards:** Currently there is no Leaderboard for this dataset. ### Languages - `Assamese (as)` - `Bengali (bn)` - `Gujarati (gu)` - `Kannada (kn)` - `Hindi (hi)` - `Malayalam (ml)` - `Marathi (mr)` - `Oriya (or)` - `Punjabi (pa)` - `Tamil (ta)` - `Telugu (te)` ## Dataset Structure ### Data Instances One random example from the `hi` dataset is given below in JSON format. ``` { "id": 8, "squad_id": "56be8e613aeaaa14008c90d3", "answer": "अमेरिकी फुटबॉल सम्मेलन", "context": "अमेरिकी फुटबॉल सम्मेलन (एएफसी) के चैंपियन डेनवर ब्रोंकोस ने नेशनल फुटबॉल कांफ्रेंस (एनएफसी) की चैंपियन कैरोलिना पैंथर्स को 24-10 से हराकर अपना तीसरा सुपर बाउल खिताब जीता।", "question": "एएफसी का मतलब क्या है?" } ``` ### Data Fields - `id (string)`: Unique identifier. - `squad_id (string)`: Unique identifier in Squad dataset. - `answer (strings)`: Answer as one of the two inputs. - `context (string)`: Context, the other input. - `question (string)`: Question, the output. ### Data Splits Here is the number of samples in each split for all the languages. Language | ISO 639-1 Code | Train | Dev | Test | ---------- | ---------- | ---------- | ---------- | ---------- | Assamese | as | 69,979 | 17,495 | 10,553 | Bengali | bn | 69,979 | 17,495 | 10,553 | Gujarati | gu | 69,979 | 17,495 | 10,553 | Hindi | hi | 69,979 | 17,495 | 10,553 | Kannada | kn | 69,979 | 17,495 | 10,553 | Malayalam | ml | 69,979 | 17,495 | 10,553 | Marathi | mr | 69,979 | 17,495 | 10,553 | Oriya | or | 69,979 | 17,495 | 10,553 | Punjabi | pa | 69,979 | 17,495 | 10,553 | Tamil | ta | 69,979 | 17,495 | 10,553 | Telugu | te | 69,979 | 17,495 | 10,553 | ## Dataset Creation ### Curation Rationale [Detailed in the paper](https://arxiv.org/abs/2203.05437) ### Source Data Squad Dataset(https://rajpurkar.github.io/SQuAD-explorer/) #### Initial Data Collection and Normalization [Detailed in the paper](https://arxiv.org/abs/2203.05437) #### Who are the source language producers? [Detailed in the paper](https://arxiv.org/abs/2203.05437) ### Annotations [More information needed] #### 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 Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders. ### Citation Information If you use any of the datasets, models or code modules, please cite the following paper: ``` @inproceedings{Kumar2022IndicNLGSM, title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages}, author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar}, year={2022}, url = "https://arxiv.org/abs/2203.05437", ``` ### Contributions [Detailed in the paper](https://arxiv.org/abs/2203.05437)

jganzabalseenka/direct_tv_vectors

--- dataset_info: features: - name: cluster_frames sequence: sequence: int64 - name: cluster_vectors sequence: sequence: float64 - name: cluster_predictions sequence: int64 - name: distances_between_clusters sequence: sequence: float64 - name: video_path dtype: string - name: different_rows list: - name: black_image dtype: bool - name: frame_number dtype: int64 - name: height dtype: int64 - name: horizontal_check dtype: bool - name: horizontal_xmax dtype: int64 - name: horizontal_xmin dtype: int64 - name: horizontal_ymax dtype: int64 - name: horizontal_ymin dtype: int64 - name: is_L_shape dtype: bool - name: vertical_check dtype: bool - name: vertical_xmax dtype: int64 - name: vertical_xmin dtype: int64 - name: vertical_ymax dtype: int64 - name: vertical_ymin dtype: int64 - name: width dtype: int64 splits: - name: train num_bytes: 50750 num_examples: 2 download_size: 0 dataset_size: 50750 --- # Dataset Card for "direct_tv_vectors" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

angry-snail/tcfd-v1

--- license: cc-by-nc-4.0 task_categories: - text-classification language: - en --- Dataset for TCFD complience

breadlicker45/wizards-of-Waverly-place-scripts

--- license: other ---

autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-79c1c0d8-10905463

--- type: predictions tags: - autotrain - evaluation datasets: - kmfoda/booksum eval_info: task: summarization model: pszemraj/led-large-book-summary metrics: ['bleu', 'perplexity'] dataset_name: kmfoda/booksum dataset_config: kmfoda--booksum dataset_split: test col_mapping: text: chapter target: summary_text --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/led-large-book-summary * Dataset: kmfoda/booksum * Config: kmfoda--booksum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

vsvasconcelos/SQuAD-pt_BR-V1.1_

--- license: mit language: - pt size_categories: - 100K<n<1M --- # Dataset Card para o SQuAD 1.1 em Português Brasil O conjunto de dados "Stanford Question Answering Dataset" ([SQuAD](https://drive.google.com/file/d/1Q0IaIlv2h2BC468MwUFmUST0EyN7gNkn/view)), para **tarefa de perguntas e respostas extrativas**, foi desenvolvido em 2016. Ele utiliza perguntas geradas a partir de **536 artigos da Wikipedia*** com **mais de 100.000 linhas** de dados. É construído na forma de uma pergunta e um contexto dos artigos da Wikipedia contendo a resposta à pergunta. [[1]](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2761899.pdf) Originalmente este dataset foi construído no idioma inglês, contudo, o grupo [Deep Learning Brasil](http://www.deeplearningbrasil.com.br/) o traduziu automaticamente e fez os ajustes manuais, gastando para isto cerca de 2 meses. [[2]](https://sol.sbc.org.br/index.php/kdmile/article/view/24974) ## Dataset Details ### Dataset Description O desenvolvedores fornecem dois arquivos: **squad-train-v1.1.json** e **squad-dev-v1.1.json**, sendo o primeiro para treinamento e o segundo para validação. Os arquivos possuem, respectivamente, as seguintes quantidades de registros: 87.510 e 17.853, totalizando assim: 105.363 registros. Percentualmente isto equivale a 83% dos dados para treinamento e 17% dos dados para validação. Após Análise Exploratória do dataset, verificou-se a existência de 7.283 'Id's repetidos no conjunto de validação, assim, ao invés de 17.853 registros, o conjunto de validação possuí 10.570 'Id's únicos. Desta forma, os 7.283 'Id's repetidos foram excluídos do dataset de validação original. No universo de Machine Learning, é comum a divisão dos datasets em: Treinamento; Validação; e Testes. Assim, por que aqui não foi fornecido os dados de Testes? Pesquisando em [[1]](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2761899.pdf), temos: > "[...] Utilizamos 80% do conjunto de dados para treinar o modelo, 10% para validação e ajuste hiperparâmetro. **Os 10% finais do conjunto > de dados são reservados para testes** e **mantidos privados** pelos criadores da equipe com o objetivo de preservar a integridade dos > modelos de resposta a perguntas." Portanto, **os autores não disponibilizaram os 10% de dados para testes**. Sendo assim, este dataset faz os seguintes ajustes: Mantem os dados originais do conjunto de treinamento e dividi o conjunto de validação em duas partes: validação (5.500 registros (5,6%) dos dados) e testes (5070 registros (5,2%) dos dados). - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure **features:** 'Id', 'title', 'context', 'question', 'ans_start', 'text' **'Id':** identificador único **'title':** Assunto do qual o contexto trata **'context':** Texto que contém a resposta para a questão **'question':** Questão respondida por meio do contexto **'ans_start':** Posição inicial da resposta à questão **'text':** Resposta à questão [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data Wikipedia #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

fyang-rai/vlogo

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 429548.0 num_examples: 6 download_size: 406378 dataset_size: 429548.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vlogo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

transformer-001/github-issues

--- dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: milestone struct: - name: url dtype: string - name: html_url dtype: string - name: labels_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: description dtype: string - name: creator struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: open_issues dtype: int64 - name: closed_issues dtype: int64 - name: state dtype: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: due_on dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 18908112 num_examples: 5000 download_size: 5112946 dataset_size: 18908112 --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_147

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1072808020.0 num_examples: 210685 download_size: 1094423199 dataset_size: 1072808020.0 --- # Dataset Card for "chunk_147" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

GoodBaiBai88/M3D-VQA

--- license: apache-2.0 ---

Thanmay/boolq-mr

--- dataset_info: features: - name: question dtype: string - name: answer dtype: bool - name: passage dtype: string - name: itv2 mr question dtype: string - name: itv2 mr passage dtype: string splits: - name: validation num_bytes: 6916121 num_examples: 3270 download_size: 3172220 dataset_size: 6916121 configs: - config_name: default data_files: - split: validation path: data/validation-* ---

Deojoandco/reddit-ah-dialog-annotations_v5

--- dataset_info: features: - name: url dtype: string - name: id dtype: string - name: num_comments dtype: int64 - name: name dtype: string - name: title dtype: string - name: body dtype: string - name: score dtype: int64 - name: upvote_ratio dtype: float64 - name: distinguished dtype: string - name: over_18 dtype: bool - name: created_utc dtype: int64 - name: comments list: - name: body dtype: string - name: created_utc dtype: float64 - name: distinguished dtype: string - name: id dtype: string - name: permalink dtype: string - name: score dtype: int64 - name: best_num_comments dtype: int64 - name: query dtype: string - name: dialog dtype: string - name: annotation_success dtype: bool - name: annotation_text dtype: string - name: turns_generated dtype: bool splits: - name: train num_bytes: 33860238 num_examples: 2921 - name: validation num_bytes: 3121972 num_examples: 293 - name: test num_bytes: 3378331 num_examples: 292 download_size: 23369920 dataset_size: 40360541 --- # Dataset Card for "reddit-ah-dialog-annotations_v5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mattyhatch/tomatoesSpoof1

--- dataset_info: features: - name: pixel_values dtype: image - name: label sequence: sequence: int64 splits: - name: train num_bytes: 673124095.0 num_examples: 557 download_size: 34937459 dataset_size: 673124095.0 --- # Dataset Card for "tomatoesSpoof1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

keremberke/blood-cell-object-detection

--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface - Biology --- <div align="center"> <img width="640" alt="keremberke/blood-cell-object-detection" src="https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['platelets', 'rbc', 'wbc'] ``` ### Number of Images ```json {'train': 255, 'test': 36, 'valid': 73} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/blood-cell-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3](https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface) ### Citation ``` @misc{ blood-cell-detection-1ekwu_dataset, title = { Blood Cell Detection Dataset }, type = { Open Source Dataset }, author = { Team Roboflow }, howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } }, url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-01-18 }, } ``` ### License Public Domain ### Dataset Summary This dataset was exported via roboflow.com on November 4, 2022 at 7:46 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 364 images. Cells are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) No image augmentation techniques were applied.

anistaluqdar/dudl

--- license: mit ---

Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized

--- license: cc-by-4.0 ---

AKAWIZ/telugu_asr_custom

--- license: unlicense ---

petar5r/evalo_faq

--- license: apache-2.0 ---

CyberHarem/ribeyrolles_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ribeyrolles/リベロール/利贝罗勒 (Girls' Frontline) This is the dataset of ribeyrolles/リベロール/利贝罗勒 (Girls' Frontline), containing 184 images and their tags. The core tags of this character are `long_hair, bangs, hair_between_eyes, very_long_hair, blue_eyes, white_hair, wavy_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 | 184 | 238.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 184 | 128.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 443 | 280.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 184 | 207.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 443 | 404.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ribeyrolles_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/ribeyrolles_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 | 7 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bandaged_leg, bare_shoulders, dress, looking_at_viewer, sitting, solo, bandaged_neck, blood_bag, blush, intravenous_drip, simple_background, barefoot, white_background, wide_sleeves, closed_mouth, detached_sleeves, side_cutout, full_body, hand_up, parted_lips | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, bandaged_leg, intravenous_drip, looking_at_viewer, bandaged_neck, blood_bag, closed_mouth, holding_gun, simple_background, white_background, assault_rifle, blue_dress, blush, wide_sleeves, bare_shoulders | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, looking_at_viewer, simple_background, solo, white_background, bandaged_neck, blush, grey_eyes, grey_hair, closed_mouth, long_sleeves | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, navel, nipples, simple_background, white_background, cum_in_pussy, grey_hair, small_breasts, spread_legs, 1boy, after_sex, after_vaginal, completely_nude, cumdrip, hetero, on_back, open_mouth, shiny_skin, solo_focus, sweat, censored, closed_mouth, collarbone, heart-shaped_pupils, looking_at_viewer, nose_blush, penis, purple_eyes, tears, tongue_out, trembling | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bandaged_leg | bare_shoulders | dress | looking_at_viewer | sitting | solo | bandaged_neck | blood_bag | blush | intravenous_drip | simple_background | barefoot | white_background | wide_sleeves | closed_mouth | detached_sleeves | side_cutout | full_body | hand_up | parted_lips | holding_gun | assault_rifle | blue_dress | grey_eyes | grey_hair | long_sleeves | navel | nipples | cum_in_pussy | small_breasts | spread_legs | 1boy | after_sex | after_vaginal | completely_nude | cumdrip | hetero | on_back | open_mouth | shiny_skin | solo_focus | sweat | censored | collarbone | heart-shaped_pupils | nose_blush | penis | purple_eyes | tears | tongue_out | trembling | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------------|:--------|:--------------------|:----------|:-------|:----------------|:------------|:--------|:-------------------|:--------------------|:-----------|:-------------------|:---------------|:---------------|:-------------------|:--------------|:------------|:----------|:--------------|:--------------|:----------------|:-------------|:------------|:------------|:---------------|:--------|:----------|:---------------|:----------------|:--------------|:-------|:------------|:----------------|:------------------|:----------|:---------|:----------|:-------------|:-------------|:-------------|:--------|:-----------|:-------------|:----------------------|:-------------|:--------|:--------------|:--------|:-------------|:------------| | 0 | 7 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | | X | X | X | X | X | X | | X | X | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | X | | X | X | | X | | X | | X | | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | | X | | | | | | | X | | X | | X | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

firstgradeai/private_processed_demo

--- dataset_info: features: - name: formatted_chat dtype: string splits: - name: train num_bytes: 134885 num_examples: 10 download_size: 78616 dataset_size: 134885 configs: - config_name: default data_files: - split: train path: data/train-* ---

mask-distilled-one-sec-cv12/chunk_170

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1237712440 num_examples: 243070 download_size: 1261211885 dataset_size: 1237712440 --- # Dataset Card for "chunk_170" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

A-Bar/nl-de_non_top_cs_train

--- dataset_info: features: - name: query dtype: string - name: passage dtype: string - name: label dtype: float64 splits: - name: train num_bytes: 409205240 num_examples: 1000000 download_size: 173282815 dataset_size: 409205240 configs: - config_name: default data_files: - split: train path: data/train-* ---

hungnm/vietnamese-medical-qa

--- dataset_info: features: - name: answer dtype: string - name: question dtype: string splits: - name: train num_bytes: 10672406 num_examples: 9335 download_size: 5289086 dataset_size: 10672406 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - vi tags: - question answering - medical - vietnamese license: apache-2.0 pretty_name: b size_categories: - 1K<n<10K --- ## Dataset Summary Vietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from [edoctor](https://edoctor.io/hoi-dap) and [vinmec](https://www.vinmec.com/vi/tin-tuc/hoi-dap-bac-si/). * Size: After merging data from these two sources, obtained 9335 QA pairs. * Language: Vietnamese ## Load with Datasets ```python from datasets import load_dataset # Load dataset from huggingface qa_dataset = load_dataset("hungnm/vietnamese-medical-qa") # print a QA example print(qa_dataset['train'][0]) ``` ```json { "question": "Chào bác sĩ,\nRăng cháu hiện tại có mủ ở dưới lợi nhưng khi đau cháu sẽ không ngủ được (quá đau). Tuy nhiên chỉ vài ngày là hết mà thỉnh thoảng nó lại bị đau. Chị cháu bảo là trước chị cháu cũng bị như vậy chỉ là đau răng tuổi dậy thì thôi. Bác sĩ cho cháu hỏi đau răng kèm có mủ dưới lợi là bệnh gì? Cháu có cần đi chữa trị không? Cháu cảm ơn.", "answer": "Chào bạn,\nĐể trả lời câu hỏi trên, bác sĩ xin giải đáp như sau:\nRăng bạn hiện tại có mủ dưới lợi gây đau nhức nhiều. Bạn có thể đến phòng khám răng hàm mặt bệnh viện để được thăm khám, chụp phim và tư vấn cho bạn được chính xác\nTrân trọng!" } ``` ## Dataset Version and Update * Version Details: * Current version: 1.0 * Last Update: 16/02/2024 * First Release: 16/02/2024 * Update Plan: We will keep updating as we gather additional data. ## Licensing Information This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License. ## Citation Information ```bibtex @misc{vietnamese-medical-qa, title={Vietnamese Medical QA: Question Answering dataset for medical in Vietnamese}, author={Hung Nguyen}, year={2024}, } ```

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b6645655

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1340 dataset_size: 184 --- # Dataset Card for "b6645655" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

taln-ls2n/kpbiomed

--- annotations_creators: - unknown language_creators: - unknown language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual task_categories: - text-mining - text-generation task_ids: - keyphrase-generation - keyphrase-extraction size_categories: - 100K<n<1M pretty_name: KP-Biomed --- # KPBiomed, A Large-Scale Dataset for keyphrase generation ## About This dataset is made of 5.6 million abstracts with author assigned keyphrases. Details about the dataset can be found in the original paper: Maël Houbre, Florian Boudin and Béatrice Daille. 2022. [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://arxiv.org/abs/2211.12124). In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI 2022). Reference (author-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper: - Florian Boudin and Ygor Gallina. 2021. [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics. Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text. ## Content The details of the dataset are in the table below: | Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen | | :----------- | ----------: | ---------------------------------: | --------: | ----------: | ------: | -------: | | Train small | 500k | 5.24 | 66.31 | 7.16 | 12.60 | 13.93 | | Train medium | 2M | 5.24 | 66.30 | 7.18 | 12.57 | 13.95 | | Train large | 5.6M | 5.23 | 66.32 | 7.18 | 12.55 | 13.95 | | Validation | 20k | 5.25 | 66.44 | 7.07 | 12.45 | 14.05 | | Test | 20k | 5.22 | 66.59 | 7.22 | 12.44 | 13.75 | The following data fields are available: - **id**: unique identifier of the document. - **title**: title of the document. - **abstract**: abstract of the document. - **keyphrases**: list of reference keyphrases. - **mesh terms**: list of indexer assigned MeSH terms if available (around 68% of the articles) - **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases. - **authors**: list of the article's authors - **year**: publication year **NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text).

met/Met

--- license: apache-2.0 ---

abwqr/pokemon_test

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4730788.764705882 num_examples: 33 download_size: 3699706 dataset_size: 4730788.764705882 --- # Dataset Card for "pokemon_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NathanRoll/TalkBank_CA_wM_Croatian

--- dataset_info: features: - name: audio sequence: float32 - name: text dtype: string - name: __index_level_0__ dtype: string splits: - name: train num_bytes: 5830164732 num_examples: 135 download_size: 5836483668 dataset_size: 5830164732 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "TalkBank_CA_wM_Croatian" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

EPFL-LNMC/channelpedia_paper_taged

--- dataset_info: features: - name: metadata struct: - name: authors sequence: string - name: id dtype: string - name: paragraph_id dtype: int64 - name: section dtype: string - name: title dtype: string - name: text dtype: string - name: embeddings sequence: float32 - name: tags sequence: string splits: - name: train num_bytes: 43188192.79975021 num_examples: 10135 download_size: 0 dataset_size: 43188192.79975021 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "channelpedia_paper_taged" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k

--- pretty_name: Evaluation run of Open-Orca/LlongOrca-13B-16k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Open-Orca/LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T02:39:50.739204](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k/blob/main/results_2023-09-23T02-39-50.739204.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.24842701342281878,\n\ \ \"em_stderr\": 0.004425115813837482,\n \"f1\": 0.3159280620805379,\n\ \ \"f1_stderr\": 0.004388510945380163,\n \"acc\": 0.4434148948074197,\n\ \ \"acc_stderr\": 0.010487468726575147\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.24842701342281878,\n \"em_stderr\": 0.004425115813837482,\n\ \ \"f1\": 0.3159280620805379,\n \"f1_stderr\": 0.004388510945380163\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12282031842304776,\n \ \ \"acc_stderr\": 0.009041108602874675\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7640094711917916,\n \"acc_stderr\": 0.011933828850275621\n\ \ }\n}\n```" repo_url: https://huggingface.co/Open-Orca/LlongOrca-13B-16k 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_08_23T10_31_15.034338 path: - '**/details_harness|arc:challenge|25_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-23T10:31:15.034338.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T02_39_50.739204 path: - '**/details_harness|drop|3_2023-09-23T02-39-50.739204.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-23T02-39-50.739204.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T02_39_50.739204 path: - '**/details_harness|gsm8k|5_2023-09-23T02-39-50.739204.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-23T02-39-50.739204.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hellaswag|10_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:31:15.034338.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-management|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T10:31:15.034338.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_23T10_31_15.034338 path: - '**/details_harness|truthfulqa:mc|0_2023-08-23T10:31:15.034338.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-23T10:31:15.034338.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T02_39_50.739204 path: - '**/details_harness|winogrande|5_2023-09-23T02-39-50.739204.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-23T02-39-50.739204.parquet' - config_name: results data_files: - split: 2023_09_23T02_39_50.739204 path: - results_2023-09-23T02-39-50.739204.parquet - split: latest path: - results_2023-09-23T02-39-50.739204.parquet --- # Dataset Card for Evaluation run of Open-Orca/LlongOrca-13B-16k ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Open-Orca/LlongOrca-13B-16k - **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 [Open-Orca/LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T02:39:50.739204](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-13B-16k/blob/main/results_2023-09-23T02-39-50.739204.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.24842701342281878, "em_stderr": 0.004425115813837482, "f1": 0.3159280620805379, "f1_stderr": 0.004388510945380163, "acc": 0.4434148948074197, "acc_stderr": 0.010487468726575147 }, "harness|drop|3": { "em": 0.24842701342281878, "em_stderr": 0.004425115813837482, "f1": 0.3159280620805379, "f1_stderr": 0.004388510945380163 }, "harness|gsm8k|5": { "acc": 0.12282031842304776, "acc_stderr": 0.009041108602874675 }, "harness|winogrande|5": { "acc": 0.7640094711917916, "acc_stderr": 0.011933828850275621 } } ``` ### 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]

neulab/tldr

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - mit multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: DocPrompting-CoNaLa tags: - code-generation - doc retrieval - retrieval augmented generation --- ## Dataset Description - **Repository:** https://github.com/shuyanzhou/docprompting - **Paper:** [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf) ### Dataset Summary This is the natural language to bash generation dataset we harvested from the English subset of [`tldr`](https://github.com/tldr-pages/tldr) We split the dataset by bash commands. Every command in the dev and test set is held out from the training set. ### Supported Tasks and Leaderboards This dataset is used to evaluate code generations. ### Languages English - Bash ## Dataset Structure ```python dataset = load_dataset("neulab/tldr") DatasetDict({ train: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 6414 }) test: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 928 }) validation: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 1845 }) }) code_docs = load_dataset("neulab/docprompting-conala", "docs") DatasetDict({ train: Dataset({ features: ['doc_id', 'doc_content'], num_rows: 439064 }) }) ``` ### Data Fields train/dev/test: - nl: The natural language intent - cmd: The reference code snippet - question_id: the unique id of a question - oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split - cmd_name: the bash command of this code snippet - tldr_cmd_name: the bash command used in tldr github repo. The `cmd_name` and `tldr_cmd_name` can be different due to naming difference - manual_exist: whether the manual exists in https://manned.org - matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token. docs: - doc_id: the id of a doc - doc_content: the content of the doc ## Dataset Creation The dataset was curated from [`tldr`](https://github.com/tldr-pages/tldr). The project aims to provide frequent usage of bash commands with natural language intents. For more details, please check the repo. ### Citation Information ``` @article{zhou2022doccoder, title={DocCoder: Generating Code by Retrieving and Reading Docs}, author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham}, journal={arXiv preprint arXiv:2207.05987}, year={2022} } ```

mstz/isolet

--- language: - en tags: - isolet - tabular_classification - binary_classification - multiclass_classification - UCI pretty_name: Isolet size_categories: - 1K<n<10K task_categories: - tabular-classification configs: - isolet license: cc --- # Isolet The [Isolet dataset](https://archive-beta.ics.uci.edu/dataset/54/isolet) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). # Configurations and tasks | **Configuration** | **Task** | Description | |-------------------|---------------------------|--------------------------| | isolet | Multiclass classification | What letter was uttered? | # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/isolet", "isolet")["train"] ```

karan451/BMI-labeled-faced

--- license: mit ---

316usman/thematic4aembed

--- dataset_info: features: - name: text dtype: string - name: thematic dtype: string - name: sub-thematic dtype: string - name: country dtype: string - name: document_url dtype: string - name: source_url dtype: string splits: - name: train num_bytes: 44691831 num_examples: 62243 download_size: 14281929 dataset_size: 44691831 configs: - config_name: default data_files: - split: train path: data/train-* ---

recoilme/train_data_dataset

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 2757788954.15 num_examples: 9050 download_size: 2575413735 dataset_size: 2757788954.15 configs: - config_name: default data_files: - split: train path: data/train-* ---

junkim100/SIL-Demo

--- license: mit ---

huggingartists/bushido-zho

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/bushido-zho" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 0.195456 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/6e5b165de8561df37790229c26b25692.959x959x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/bushido-zho"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">BUSHIDO ZHO</div> <a href="https://genius.com/artists/bushido-zho"> <div style="text-align: center; font-size: 14px;">@bushido-zho</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/bushido-zho). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/bushido-zho") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |91| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/bushido-zho") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

Eskobar/NepaliHandSign

--- license: creativeml-openrail-m ---

notrichardren/misconceptions_tf

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: Topic dtype: string - name: Question dtype: string - name: Correct dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 181025 num_examples: 1703 download_size: 83862 dataset_size: 181025 --- # Dataset Card for "misconceptions_tf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nuratamton/StorySculptor_dataset

--- language: - en license: gpl-3.0 ---

vaishaal/ImageNetV2

--- license: mit ---

didi0di/KoChatGPT

--- license: unknown dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: tokens dtype: int64 splits: - name: train num_bytes: 4902713 num_examples: 12000 download_size: 2876495 dataset_size: 4902713 task_categories: - text-generation language: - ko tags: - instruction - SFT size_categories: - 10K<n<100K --- # Dataset Summary ## 1.2.1) 한국어 QA 데이터셋의 질문으로 답변 자동 생성(ChatGPT): 12,000개 - SFT 데이터: data/train - 다음 단어만 잘 생성하는 모델에서 질문에 '잘' 대답하는 모델 만들기 - Human labling이 필요하지만 ChatGPT 이용 - 질문을 입력으로 주고 ChatGPT로 답변 자동생성 ### 데이터셋 예시 ``` { "prompt": "술 먹고 싶어", "completion": "'술은 알코올이 함유된 음료수이며, 건강에 나쁜 영향을 미칠 수 있습니다. 따라서 술을 마시기 전에 본인의 건강 상태와 함께 적당한 양을 마시는 것이 중요합니다. 술을 마시기 전에 반드시 20세 이상인지 확인하시고, 규정된 양 이내에서 적절하게 즐기시길 바랍니다. 또한 술을 마시고 운전하는 것은 절대 금지하시기 바랍니다.", "tokens": 189 } ``` ## 1.2.2) langchain을 이용한 채팅데이터 자동생성(ChatGPT): 439개 - kochatgpt_1_SFT_conversation.jsonl : 한국어 질문 수집 데이터셋 - 다음 단어만 잘 생성하는 모델에서 질문에 '잘' 대답하는 모델 만들기 - ChatGPT로 사람과 AI가 대화를 주고받는 채팅 데이터 생성 ### 데이터셋 예시 ``` <<start conversation>> <사람>: 그렇군요. 무엇에 대해 이야기하고 싶으세요? <챗봇>: 궁금하신 주제에 대해 이야기하고 싶으신가요? 예를 들어 여행, 식품, 스포츠, 등등 다양한 토론에 참여할 수 있습니다. <사람>: 그래서 스포츠에 대해 이야기해볼까요? <챗봇>: 그렇습니다. 스포츠에 대해 말씀하시겠습니까? 스포츠는 다양한 종목과 관련된 주제로 비슷한 경기를 비교하거나 새로운 경기를 분석할 수 있습니다. 또한 스포츠 관련 소식과 업데이트 등을 제공할 수 있습니다. <사람>: 괜찮습니다. 소식이나 업데이트는 어떻게 받을 수 있나요? <챗봇>: 귀하는 스포츠 관련 소식과 업데이트를 접할 수 있는 다양한 방법을 사용할 수 있습니다. 예를 들면 스포츠 사이트, 스포츠 앱, 스포츠 유튜브 채널 등을 통해 스포츠 소식과 업데이트를 받을 수 있습니다. 또한 소셜 미디어에서도 스포츠 소식과 업데이트를 받을 수 있습니다. <사람>: 감사합니다. 이제 스포츠 소식과 업데이트를 받아볼 준비가 되었습니다. <챗봇>: 그렇게 되었군요. 스포츠 소식과 업데이트를 받기 위해 귀하가 해야 할 준비는 무엇인가요? 스포츠 사이트, 스포츠 앱, 스포츠 유튜브 채널, 소셜 미디어 등을 통해 스포츠 소식과 업데이트를 받기 위해 준비해야 합니다. ``` ## Data Source https://github.com/airobotlab/KoChatGPT/tree/main

BEE-spoke-data/govdocs1-txt-raw

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: section dtype: string - name: filename dtype: string - name: text dtype: string splits: - name: train num_bytes: 16101385278.039026 num_examples: 67984 - name: validation num_bytes: 894547719.9804866 num_examples: 3777 - name: test num_bytes: 894547719.9804866 num_examples: 3777 download_size: 7656656755 dataset_size: 17890480718 license: odc-by task_categories: - text-generation size_categories: - 10K<n<100K --- # Dataset Card for "govdocs1-txt-raw" Somewhere to put the raw txt files before filtering them Source info/page: https://digitalcorpora.org/corpora/file-corpora/files/ ``` @inproceedings{garfinkel2009bringing, title={Bringing Science to Digital Forensics with Standardized Forensic Corpora}, author={Garfinkel, Simson and Farrell, Paul and Roussev, Vassil and Dinolt, George}, booktitle={Digital Forensic Research Workshop (DFRWS) 2009}, year={2009}, address={Montreal, Canada}, url={https://digitalcorpora.org/corpora/file-corpora/files/} } ```

MartinDx/second

--- license: mit ---

antolin/codealpaca-filtered

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: nl dtype: string - name: cmd dtype: string splits: - name: train num_bytes: 611759.1279592449 num_examples: 2194 - name: test num_bytes: 175106.98831285586 num_examples: 628 - name: valid num_bytes: 87553.49415642793 num_examples: 314 download_size: 447020 dataset_size: 874419.6104285286 --- # Dataset Card for "codealpaca-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NaturalTeam/KoBART_TEST

--- license: unknown ---

mayalenE/simple-foc-discourse

--- license: mit ---

p1atdev/202312-at20240326-safe

--- dataset_info: features: - name: id dtype: int64 - name: copyright dtype: string - name: character dtype: string - name: artist dtype: string - name: general dtype: string - name: meta dtype: string - name: rating dtype: string - name: score dtype: int64 - name: created_at dtype: string splits: - name: train num_bytes: 741938657 num_examples: 1487516 download_size: 280384451 dataset_size: 741938657 configs: - config_name: default data_files: - split: train path: data/train-* ---

nroggendorff/nebulae

--- license: mit size_categories: - 1K<n<10K task_categories: - image-classification - unconditional-image-generation tags: - art dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 231315247.272 num_examples: 1657 download_size: 265723176 dataset_size: 231315247.272 configs: - config_name: default data_files: - split: train path: data/train-* --- ## Nebula Image Dataset ### Overview This dataset contains a collection of images depicting various types of nebulas. Nebulas are large, diffuse astronomical objects composed of ionized gases, dust, and plasma. They are often found near star-forming regions and are an important part of the life cycle of stars. ### Dataset Description The dataset includes about one hundred high-resolution images of different nebulas, such as emission nebulas, reflection nebulas, and planetary nebulas. The images were collected from public astronomical sources and have been carefully curated to ensure high quality and diversity. ### Data Format The dataset is stored in the Parquet file format, which is a columnar data format that provides efficient storage and query performance. The Parquet file contains a single column, `train`, which holds the file paths to the individual nebula images. ## Usage You can use this dataset for a variety of machine learning and computer vision tasks, such as: - Nebula classification - Nebula segmentation - Nebula generation or synthesis - Astronomical image processing and analysis To get started, cite the [datasets section](https://huggingface.co/datasets/nroggendorff/nebulae#datasets). ### License This dataset is licensed under the [MIT License](LICENSE). You are free to use, modify, and distribute the data as long as you provide attribution. ### Datasets ```py from datasets import load_dataset config.dataset_name = "nroggendorff/nebulae" dataset = load_dataset(config.dataset_name, split="image") ``` ### Acknowledgements The images in this dataset were collected from various public astronomical sources. We would like to thank the astronomers and organizations who have contributed to the advancement of our understanding of nebulas and other celestial phenomena.

shidowake/glaive-code-assistant-v1-sharegpt-format_split_12

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 10503837.603832223 num_examples: 6805 download_size: 5171549 dataset_size: 10503837.603832223 configs: - config_name: default data_files: - split: train path: data/train-* ---

heliosprime/twitter_dataset_1713215686

--- 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: 18980 num_examples: 54 download_size: 18379 dataset_size: 18980 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713215686" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

redwoodresearch/wmdp-cyber-deduped

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: dev num_bytes: 1772 num_examples: 5 - name: split_0 num_bytes: 36846 num_examples: 125 - name: split_1 num_bytes: 39721 num_examples: 125 - name: split_2 num_bytes: 39199 num_examples: 125 - name: split_3 num_bytes: 37203 num_examples: 125 - name: split_4 num_bytes: 39729 num_examples: 125 download_size: 139755 dataset_size: 194470 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: split_0 path: data/split_0-* - split: split_1 path: data/split_1-* - split: split_2 path: data/split_2-* - split: split_3 path: data/split_3-* - split: split_4 path: data/split_4-* ---

polinaeterna/test_verifications_checksums

--- dataset_info: features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 160 num_examples: 1 - name: test num_bytes: 8 num_examples: 5 download_size: 2688 dataset_size: 240 --- # Dataset Card for "test_verifications_checksums" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gcx/new_

--- license: other ---

LLMao/2024_03_10_03_19_49_Archive

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: content dtype: string - name: text dtype: string splits: - name: train num_bytes: 5251835 num_examples: 1433 download_size: 855697 dataset_size: 5251835 configs: - config_name: default data_files: - split: train path: data/train-* ---

ninoscherrer/moralchoice

--- pretty_name: MoralChoice license: cc-by-4.0 language: - en size_categories: - 1K<n<10K --- # Dataset Card for MoralChoice - **Homepage:** Coming Soon - **Paper:** Coming soon - **Repository:** [https://github.com/ninodimontalcino/moralchoice](https://github.com/ninodimontalcino/moralchoice) - **Point of Contact:** [Nino Scherrer & Claudia Shi](mailto:nino.scherrer@gmail.com,claudia.j.shi@gmail.com?subject=[MoralChoice]) ### Dataset Summary *MoralChoice* is a survey dataset to evaluate the moral beliefs encoded in LLMs. The dataset consists of: - **Survey Question Meta-Data:** 1767 hypothetical moral scenarios where each scenario consists of a description / context and two potential actions - **Low-Ambiguity Moral Scenarios (687 scenarios):** One action is clearly preferred over the other. - **High-Ambiguity Moral Scenarios (680 scenarios):** Neither action is clearly preferred - **Survey Question Templates:** 3 hand-curated question templates - **Survey Responses:** Outputs from 28 open- and closed-sourced LLMs A statistical workflow for analyzing the survey responses can be found in the corresponding [paper](). 🚧 **Important**: 🚧 - *Moral scenarios* and *question templates* are already available. - *Survey responses* will be uploaded shortly! ### Languages *MoralChoice* is only available in English. ## Dataset Structure ### Data Fields #### Moral Scenarios (Survey Question Meta-Data) ``` - scenario_id unique scenario identifier - ambiguity level of ambiguity (low or high) - generation_type generation type (hand-written or generated) - context scenario description / contextualization - action 1 description of a potential action - action 2 description of a potential action - a1_{rule} {rule} violation label of action 1 - a2_{rule} {rule} violation label of action 2 ``` #### Survey Question Templates ``` - name name of question template (e.g., ab, repeat, compare) - question_header question instruction header text - question question template with placeholders ``` #### Survey Responses ``` - scenario_id unique scenario identifier - model_id model identifier (e.g., openai/gpt-4) - question_type question type (ab: A or B?, repeat: Repeat the preferred answer, compare: Do you prefer A over B? ) - question_ordering question ordering label (0: default order, 1: flipped order) - question_header question instruction header text - question_text question text - answer_raw raw answer of model - decision semantic answer of model (e.g., action1, action2, refusal, invalid) - eval_technique evaluation technique used - eval_top_p evaluation parameter - top_p - eval_temperature evaluation parameter - temperature - timestamp timestamp of model access ``` ## Dataset Creation ### Generation of Moral Scenarios The construction of *MoralChoice* follows a three-step procedure: - **Scenario Generation:** We generate seperately low- and high-ambiguity scenarios (i.e., the triple of scenario context, action 1 and action 2) guided by the 10 rules of Gert's common morality framework. - **Low-Ambiguity Scenarios:** Zero-Shot Prompting Setup based on OpenAI's gpt-4 - **High-Ambiguity Scenarios:** Stochastic Few-Shot Prompting Setup based on OpenAI's text-davinci-003 using a a set of 100 hand-written scenarios - **Scenario Curation:** We check the validity and grammar of each generated scenario manually and remove invalid scenarios. In addition, we assess lexical similarity between the generated scenarios and remove duplicates and overly-similar scenarios. - **Auxiliarly Label Aquisition:** We acquire auxiliary rule violation labels through SurgeAI for every scenario. For detailed information, we refer to the corresponding paper. ## Collection of LLM responses Across all models, we employ **temperature-based sampling** with `top-p=1.0`and `temperature=1.0`. For every specific question form (unique combination of scenario, question template, answer option ordering), we collect multiple samples (5 for low-ambiguity scenarios and 10 for high-ambiguity scenarios). The raw sequence of token outputs were mapped to semantic action (see the corresponding paper for exact details). ### Annotations To acquire high-quality annotations, we employ experienced annotators sourced through the data-labeling company [Surge AI](https://www.surgehq.ai/). ## Considerations for Using the Data - Limited Diversity in Scenarios (professions, contexts) - Limited Diversity in Question-Templates - Limited to English ### Dataset Curators - Nino Scherrer ([Website](https://ninodimontalcino.github.io/), [Mail](mailto:nino.scherrer@gmail.com?subject=[MoralChoice])) - Claudia Shi ([Website](https://www.claudiajshi.com/), [Mail](mailto:nino.scherrer@gmail.com?subject=[MoralChoice])) ### Citation ``` @misc{scherrer2023moralchoice, title={Evaluating the Moral Beliefs Encoded in LLMs}, author={Scherrer, Nino and Shi, Claudia, and Feder, Amir and Blei, David}, year={2023}, journal={arXiv:} } ```

Deepexi/glaive-function-calling-vicuna

--- license: cc-by-4.0 language: - en size_categories: - 10K<n<100K --- **数据集格式说明： [[glaiveai/glaive-function-calling · Datasets at Hugging Face](https://huggingface.co/datasets/glaiveai/glaive-function-calling)](glaiveai/glaive-function-calling) 的 SFT 格式** 我们高兴地宣布，数据集 "glaiveai/glaive-function-calling" 已经根据 SFT（Supervised Fine-Tuning）的需求进行了格式转换，以支持大型语言模型的训练。以下是有关这一新格式的简要说明： 1. **数据集概述:** 数据集 "glaiveai/glaive-function-calling" 基于 CC-BY-4.0 协议发布，原始数据集包含标识符和对话信息，这些数据已被转换为适应 SFT 训练的结构。 2. **数据格式：** 转换后的数据集格式包含以下关键信息： - `id`: 整数类型的标识符，用于唯一标识每个数据样本。 - `conversations`: 一个数组，其中包含对话信息。每个对话可以由多个句子组成，以更好地呈现函数调用的上下文。 3. **数据集用途：** 转换后的数据集适用于 SFT 的训练，主要用途包括但不限于： - 函数调用理解：通过分析对话中的函数调用信息，让语言模型更好地理解函数之间的关系，从而提高其代码理解能力。 - 上下文感知性：对话信息能够为模型提供更丰富的上下文，使其更准确地推断和生成代码片段。 - 代码生成与推荐：基于对话中的函数调用上下文，模型可以更精确地生成代码，并提供更合适的函数建议。通过将数据集 "glaiveai/glaive-function-calling" 转换为 SFT 格式，我们旨在为大型语言模型的训练提供更适合sft的函数调用数据，以提升其代码理解和生成的性能。如有任何问题或需要进一步帮助，请随时联系我们。感谢您对函数调用数据集及其应用的兴趣与支持！

bz-arc13/sharegpt-chinese

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 201910458 num_examples: 30015 download_size: 101105044 dataset_size: 201910458 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_jondurbin__airoboros-34b-3.3

--- pretty_name: Evaluation run of jondurbin/airoboros-34b-3.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jondurbin/airoboros-34b-3.3](https://huggingface.co/jondurbin/airoboros-34b-3.3)\ \ 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_jondurbin__airoboros-34b-3.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-03T06:36:09.032058](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-34b-3.3/blob/main/results_2024-04-03T06-36-09.032058.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\": 0.7384979768935336,\n\ \ \"acc_stderr\": 0.028846149322558155,\n \"acc_norm\": 0.7486960506623671,\n\ \ \"acc_norm_stderr\": 0.02940148738177012,\n \"mc1\": 0.42472460220318237,\n\ \ \"mc1_stderr\": 0.017304000957167477,\n \"mc2\": 0.5958885847450273,\n\ \ \"mc2_stderr\": 0.015660972854187622\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042198,\n\ \ \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693247\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6443935471021709,\n\ \ \"acc_stderr\": 0.0047771835089498085,\n \"acc_norm\": 0.8444532961561442,\n\ \ \"acc_norm_stderr\": 0.0036168436913607657\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7185185185185186,\n\ \ \"acc_stderr\": 0.03885004245800253,\n \"acc_norm\": 0.7185185185185186,\n\ \ \"acc_norm_stderr\": 0.03885004245800253\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \ \ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\ \ \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \ \ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8188679245283019,\n \"acc_stderr\": 0.023702963526757787,\n\ \ \"acc_norm\": 0.8188679245283019,\n \"acc_norm_stderr\": 0.023702963526757787\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.875,\n\ \ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \ \ \"acc_norm_stderr\": 0.02765610492929436\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\":\ \ 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267437,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267437\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\": 0.81,\n\ \ \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.81,\n \ \ \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.774468085106383,\n \"acc_stderr\": 0.027321078417387536,\n\ \ \"acc_norm\": 0.774468085106383,\n \"acc_norm_stderr\": 0.027321078417387536\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5701754385964912,\n\ \ \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.5701754385964912,\n\ \ \"acc_norm_stderr\": 0.04657047260594964\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\"\ : 0.6772486772486772,\n \"acc_stderr\": 0.024078943243597016,\n \"\ acc_norm\": 0.6772486772486772,\n \"acc_norm_stderr\": 0.024078943243597016\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\"\ : 0.9,\n \"acc_stderr\": 0.017066403719657258,\n \"acc_norm\": 0.9,\n\ \ \"acc_norm_stderr\": 0.017066403719657258\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.645320197044335,\n \"acc_stderr\": 0.0336612448905145,\n\ \ \"acc_norm\": 0.645320197044335,\n \"acc_norm_stderr\": 0.0336612448905145\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781657,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781657\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\ \ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7692307692307693,\n \"acc_stderr\": 0.021362027725222717,\n\ \ \"acc_norm\": 0.7692307692307693,\n \"acc_norm_stderr\": 0.021362027725222717\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.4111111111111111,\n \"acc_stderr\": 0.029999923508706675,\n \ \ \"acc_norm\": 0.4111111111111111,\n \"acc_norm_stderr\": 0.029999923508706675\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8319327731092437,\n \"acc_stderr\": 0.024289102115692275,\n\ \ \"acc_norm\": 0.8319327731092437,\n \"acc_norm_stderr\": 0.024289102115692275\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3973509933774834,\n \"acc_stderr\": 0.0399552400768168,\n \"acc_norm\"\ : 0.3973509933774834,\n \"acc_norm_stderr\": 0.0399552400768168\n },\n\ \ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9100917431192661,\n\ \ \"acc_stderr\": 0.012264304540230435,\n \"acc_norm\": 0.9100917431192661,\n\ \ \"acc_norm_stderr\": 0.012264304540230435\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\ : {\n \"acc\": 0.6064814814814815,\n \"acc_stderr\": 0.03331747876370312,\n\ \ \"acc_norm\": 0.6064814814814815,\n \"acc_norm_stderr\": 0.03331747876370312\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9068627450980392,\n \"acc_stderr\": 0.020397853969427,\n \"acc_norm\"\ : 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969427\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.9071729957805907,\n \"acc_stderr\": 0.018889750550956715,\n \"\ acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.018889750550956715\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951538,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951538\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622804,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622804\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.02923927267563275,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.02923927267563275\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.026845765054553855,\n\ \ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.026845765054553855\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.046161430750285455,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.046161430750285455\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.033932957297610096,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.033932957297610096\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132366,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132366\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8876117496807152,\n\ \ \"acc_stderr\": 0.011294541351216554,\n \"acc_norm\": 0.8876117496807152,\n\ \ \"acc_norm_stderr\": 0.011294541351216554\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8005780346820809,\n \"acc_stderr\": 0.021511900654252552,\n\ \ \"acc_norm\": 0.8005780346820809,\n \"acc_norm_stderr\": 0.021511900654252552\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.740782122905028,\n\ \ \"acc_stderr\": 0.014655780837497722,\n \"acc_norm\": 0.740782122905028,\n\ \ \"acc_norm_stderr\": 0.014655780837497722\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.02229285828456807,\n\ \ \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02229285828456807\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8038585209003215,\n\ \ \"acc_stderr\": 0.022552447780478033,\n \"acc_norm\": 0.8038585209003215,\n\ \ \"acc_norm_stderr\": 0.022552447780478033\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.019935086092149886,\n\ \ \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.019935086092149886\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6205673758865248,\n \"acc_stderr\": 0.0289473388516141,\n \ \ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.0289473388516141\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5697522816166884,\n\ \ \"acc_stderr\": 0.012645361435115228,\n \"acc_norm\": 0.5697522816166884,\n\ \ \"acc_norm_stderr\": 0.012645361435115228\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7904411764705882,\n \"acc_stderr\": 0.02472311040767708,\n\ \ \"acc_norm\": 0.7904411764705882,\n \"acc_norm_stderr\": 0.02472311040767708\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7990196078431373,\n \"acc_stderr\": 0.016211938889655574,\n \ \ \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.016211938889655574\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8244897959183674,\n \"acc_stderr\": 0.024352800722970018,\n\ \ \"acc_norm\": 0.8244897959183674,\n \"acc_norm_stderr\": 0.024352800722970018\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\ \ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\ \ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\ \ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\ \ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.026168221344662297,\n\ \ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.026168221344662297\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42472460220318237,\n\ \ \"mc1_stderr\": 0.017304000957167477,\n \"mc2\": 0.5958885847450273,\n\ \ \"mc2_stderr\": 0.015660972854187622\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047986\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2971948445792267,\n \ \ \"acc_stderr\": 0.012588685966624182\n }\n}\n```" repo_url: https://huggingface.co/jondurbin/airoboros-34b-3.3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|arc:challenge|25_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-03T06-36-09.032058.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|gsm8k|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hellaswag|10_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-03T06-36-09.032058.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-management|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T06-36-09.032058.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|truthfulqa:mc|0_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-03T06-36-09.032058.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_03T06_36_09.032058 path: - '**/details_harness|winogrande|5_2024-04-03T06-36-09.032058.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-03T06-36-09.032058.parquet' - config_name: results data_files: - split: 2024_04_03T06_36_09.032058 path: - results_2024-04-03T06-36-09.032058.parquet - split: latest path: - results_2024-04-03T06-36-09.032058.parquet --- # Dataset Card for Evaluation run of jondurbin/airoboros-34b-3.3  Dataset automatically created during the evaluation run of model [jondurbin/airoboros-34b-3.3](https://huggingface.co/jondurbin/airoboros-34b-3.3) 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_jondurbin__airoboros-34b-3.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-03T06:36:09.032058](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-34b-3.3/blob/main/results_2024-04-03T06-36-09.032058.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": 0.7384979768935336, "acc_stderr": 0.028846149322558155, "acc_norm": 0.7486960506623671, "acc_norm_stderr": 0.02940148738177012, "mc1": 0.42472460220318237, "mc1_stderr": 0.017304000957167477, "mc2": 0.5958885847450273, "mc2_stderr": 0.015660972854187622 }, "harness|arc:challenge|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042198, "acc_norm": 0.674061433447099, "acc_norm_stderr": 0.013697432466693247 }, "harness|hellaswag|10": { "acc": 0.6443935471021709, "acc_stderr": 0.0047771835089498085, "acc_norm": 0.8444532961561442, "acc_norm_stderr": 0.0036168436913607657 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7185185185185186, "acc_stderr": 0.03885004245800253, "acc_norm": 0.7185185185185186, "acc_norm_stderr": 0.03885004245800253 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8188679245283019, "acc_stderr": 0.023702963526757787, "acc_norm": 0.8188679245283019, "acc_norm_stderr": 0.023702963526757787 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.875, "acc_stderr": 0.02765610492929436, "acc_norm": 0.875, "acc_norm_stderr": 0.02765610492929436 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267437, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267437 }, "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": 0.81, "acc_stderr": 0.03942772444036624, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036624 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.774468085106383, "acc_stderr": 0.027321078417387536, "acc_norm": 0.774468085106383, "acc_norm_stderr": 0.027321078417387536 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5701754385964912, "acc_stderr": 0.04657047260594964, "acc_norm": 0.5701754385964912, "acc_norm_stderr": 0.04657047260594964 }, "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": 0.6772486772486772, "acc_stderr": 0.024078943243597016, "acc_norm": 0.6772486772486772, "acc_norm_stderr": 0.024078943243597016 }, "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": 0.9, "acc_stderr": 0.017066403719657258, "acc_norm": 0.9, "acc_norm_stderr": 0.017066403719657258 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.645320197044335, "acc_stderr": 0.0336612448905145, "acc_norm": 0.645320197044335, "acc_norm_stderr": 0.0336612448905145 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.042295258468165044, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165044 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781657, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781657 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7692307692307693, "acc_stderr": 0.021362027725222717, "acc_norm": 0.7692307692307693, "acc_norm_stderr": 0.021362027725222717 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4111111111111111, "acc_stderr": 0.029999923508706675, "acc_norm": 0.4111111111111111, "acc_norm_stderr": 0.029999923508706675 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8319327731092437, "acc_stderr": 0.024289102115692275, "acc_norm": 0.8319327731092437, "acc_norm_stderr": 0.024289102115692275 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3973509933774834, "acc_stderr": 0.0399552400768168, "acc_norm": 0.3973509933774834, "acc_norm_stderr": 0.0399552400768168 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9100917431192661, "acc_stderr": 0.012264304540230435, "acc_norm": 0.9100917431192661, "acc_norm_stderr": 0.012264304540230435 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6064814814814815, "acc_stderr": 0.03331747876370312, "acc_norm": 0.6064814814814815, "acc_norm_stderr": 0.03331747876370312 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9068627450980392, "acc_stderr": 0.020397853969427, "acc_norm": 0.9068627450980392, "acc_norm_stderr": 0.020397853969427 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9071729957805907, "acc_stderr": 0.018889750550956715, "acc_norm": 0.9071729957805907, "acc_norm_stderr": 0.018889750550956715 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8549618320610687, "acc_stderr": 0.03088466108951538, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.03088466108951538 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622804, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622804 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8981481481481481, "acc_stderr": 0.02923927267563275, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.02923927267563275 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8650306748466258, "acc_stderr": 0.026845765054553855, "acc_norm": 0.8650306748466258, "acc_norm_stderr": 0.026845765054553855 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6160714285714286, "acc_stderr": 0.046161430750285455, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.046161430750285455 }, "harness|hendrycksTest-management|5": { "acc": 0.8640776699029126, "acc_stderr": 0.033932957297610096, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.033932957297610096 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132366, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132366 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8876117496807152, "acc_stderr": 0.011294541351216554, "acc_norm": 0.8876117496807152, "acc_norm_stderr": 0.011294541351216554 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8005780346820809, "acc_stderr": 0.021511900654252552, "acc_norm": 0.8005780346820809, "acc_norm_stderr": 0.021511900654252552 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.740782122905028, "acc_stderr": 0.014655780837497722, "acc_norm": 0.740782122905028, "acc_norm_stderr": 0.014655780837497722 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8137254901960784, "acc_stderr": 0.02229285828456807, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.02229285828456807 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8038585209003215, "acc_stderr": 0.022552447780478033, "acc_norm": 0.8038585209003215, "acc_norm_stderr": 0.022552447780478033 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8487654320987654, "acc_stderr": 0.019935086092149886, "acc_norm": 0.8487654320987654, "acc_norm_stderr": 0.019935086092149886 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6205673758865248, "acc_stderr": 0.0289473388516141, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.0289473388516141 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5697522816166884, "acc_stderr": 0.012645361435115228, "acc_norm": 0.5697522816166884, "acc_norm_stderr": 0.012645361435115228 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7904411764705882, "acc_stderr": 0.02472311040767708, "acc_norm": 0.7904411764705882, "acc_norm_stderr": 0.02472311040767708 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7990196078431373, "acc_stderr": 0.016211938889655574, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.016211938889655574 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8244897959183674, "acc_stderr": 0.024352800722970018, "acc_norm": 0.8244897959183674, "acc_norm_stderr": 0.024352800722970018 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness|hendrycksTest-virology|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8654970760233918, "acc_stderr": 0.026168221344662297, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.026168221344662297 }, "harness|truthfulqa:mc|0": { "mc1": 0.42472460220318237, "mc1_stderr": 0.017304000957167477, "mc2": 0.5958885847450273, "mc2_stderr": 0.015660972854187622 }, "harness|winogrande|5": { "acc": 0.8208366219415943, "acc_stderr": 0.010777949156047986 }, "harness|gsm8k|5": { "acc": 0.2971948445792267, "acc_stderr": 0.012588685966624182 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

distilled-from-one-sec-cv12/chunk_240

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1464626612 num_examples: 285391 download_size: 1496867773 dataset_size: 1464626612 --- # Dataset Card for "chunk_240" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Cerrisete07/Raimundo

--- license: other ---

pradeep239/plain_penBlackW_250_PdfS

--- license: mit dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 386873256.0 num_examples: 680 - name: validation num_bytes: 44601877.0 num_examples: 80 - name: test num_bytes: 22237112.0 num_examples: 41 download_size: 365880585 dataset_size: 453712245.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

aquillesdaamizade/pony-head

--- license: other ---

ekazuki/embedding-to-group

--- dataset_info: features: - name: group dtype: string - name: embedding sequence: float64 splits: - name: train num_bytes: 965694758 num_examples: 313251 download_size: 814149184 dataset_size: 965694758 configs: - config_name: default data_files: - split: train path: data/train-* ---

lprat/wiki_articles_data

--- dataset_info: features: - name: texts dtype: string - name: questions dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 42832878 num_examples: 10001 download_size: 4511591 dataset_size: 42832878 --- # Dataset Card for "wiki_articles_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

euclaise/MegaCoT

--- dataset_info: features: - name: rationale dtype: string - name: target dtype: string - name: source dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 189154865 num_examples: 629377 download_size: 90889666 dataset_size: 189154865 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for MegaCoT Slightly more detailed (to allow for RL) and extended re-creation of Flan's CoT submix, excluding ecqa (which contains synthetic generations). Extra datasets not in the original CoT collection: - [ROPES](https://huggingface.co/datasets/ropes) - [Entailmentbank](https://github.com/allenai/entailment_bank) (excluding task 2) - edited to include random unrelated context points in some examples - [MATH](https://huggingface.co/datasets/competition_math) (up to level 2) - [cos_e](https://huggingface.co/datasets/cos_e) - [feasibilityQA](https://huggingface.co/datasets/tasksource/feasibilityQA) - [TAL-SCQ5K](https://huggingface.co/datasets/math-eval/TAL-SCQ5K) - [Quartz](https://huggingface.co/datasets/quartz) - [boolq](https://huggingface.co/datasets/boolq) - [FLUTE](https://huggingface.co/datasets/ColumbiaNLP/FLUTE) I reserve no rights to the dataset, but the original datasets were made available under various public licenses. Hence, consider each subset of this dataset to be licensed as the original dataset from where it comes was.

carloscapote/es.wiktionary.org

--- license: cc-by-sa-4.0 language: - es pretty_name: Spanish Wiktionary size_categories: - 100K<n<1M --- # Spanish Wiktionary ## Motivation Multilingual datasets based in Wikimedia Foundation's Wiktionary tend to use its translation system to fetch non English words, what causes a lot of words and definitions being discarded. ## Development In order to solve this, I wrote a [custom parser](https://github.com/elcapo/eswiktionary_parser) that obtains the definitions straight from a dump of the Spanish Wiktionary. Both the parser and the dataset will be developed in harmony. ## Stage Both the parser and this dataset are in a very early stage of development but they already provide a list of 873.990 definitions that are easy to read and process for machine learning purposes. ## Contact Feel free to [contact me](https://github.com/elcapo) if you are interested in contributing with either the parser, or the dataset.

dar-tau/test-test

--- dataset_info: features: - name: logit_loss dtype: float64 - name: log_kl_loss dtype: float64 - name: mask_weight dtype: float64 - name: probe_loss dtype: float64 - name: top_k_acc dtype: float64 - name: attention_maps sequence: float32 - name: attention_maps_shape sequence: int64 splits: - name: train num_bytes: 83472 num_examples: 3 download_size: 11666 dataset_size: 83472 configs: - config_name: default data_files: - split: train path: data/train-* ---

BLACKBUN/paediatrics_abdominal_pain

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: Disease dtype: string - name: Explanation dtype: string splits: - name: train num_bytes: 393173 num_examples: 143 download_size: 165813 dataset_size: 393173 --- # Dataset Card for "paediatrics_abdominal_pain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Codec-SUPERB/mridangam_synth

--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: id dtype: string splits: - name: original num_bytes: 543097298.0 num_examples: 6977 - name: academicodec_hifi_16k_320d num_bytes: 179183122.103 num_examples: 6977 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 179183122.103 num_examples: 6977 - name: academicodec_hifi_24k_320d num_bytes: 268488722.103 num_examples: 6977 - name: audiodec_24k_320d num_bytes: 272675795.103 num_examples: 6977 - name: dac_16k num_bytes: 181415762.103 num_examples: 6977 - name: dac_24k num_bytes: 271837682.103 num_examples: 6977 - name: dac_44k num_bytes: 499022756.103 num_examples: 6977 - name: encodec_24k_12bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_1_5bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_24bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_3bps num_bytes: 271837682.103 num_examples: 6977 - name: encodec_24k_6bps num_bytes: 271837682.103 num_examples: 6977 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 181415762.103 num_examples: 6977 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 181415762.103 num_examples: 6977 - name: speech_tokenizer_16k num_bytes: 183648402.103 num_examples: 6977 download_size: 2585326580 dataset_size: 5026235644.957 --- # Dataset Card for "mridangam_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

torchgeo/ChesapeakeRSC

--- license: mit task_categories: - image-segmentation size_categories: - 10K<n<100K tags: - remote sensing - road segmentation ---

CyberHarem/clukay_neuralcloud

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of clukay/クルカイ/可露凯 (Neural Cloud) This is the dataset of clukay/クルカイ/可露凯 (Neural Cloud), containing 500 images and their tags. The core tags of this character are `long_hair, green_eyes, bangs, hair_ornament, facial_mark, breasts, grey_hair, hat, blunt_bangs, 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 | 500 | 816.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 416.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1299 | 943.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 700.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1299 | 1.38 GiB | [Download](https://huggingface.co/datasets/CyberHarem/clukay_neuralcloud/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/clukay_neuralcloud', 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 | 49 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, h&k_hk416, jacket, solo, assault_rifle, beret, holding_gun, white_gloves, looking_at_viewer, black_thighhighs, long_sleeves, black_headwear, pleated_skirt, heckler_&_koch, plaid_skirt, teardrop, medium_breasts, trigger_discipline, bag, blush, white_hair | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_thighhighs, jacket, looking_at_viewer, solo, white_background, beret, black_headwear, long_sleeves, medium_breasts, pleated_skirt, simple_background, blush, white_gloves, plaid_skirt, closed_mouth, black_skirt, white_hair, zettai_ryouiki | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, jacket, looking_at_viewer, solo, upper_body, beret, white_background, blush, long_sleeves, medium_breasts, simple_background, white_gloves, black_headwear, closed_mouth, teardrop, white_hair, clothes_writing | | 3 | 16 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, black_gloves, official_alternate_costume, solo, strapless_dress, cleavage, collarbone, layered_dress, looking_at_viewer, white_hairband, black_thighhighs, blush, flower, large_breasts, closed_mouth, black_choker, earrings, hair_ribbon, purple_dress, smile, teardrop, simple_background, white_background, medium_breasts, multi-tied_hair, skirt_hold, bare_arms | | 4 | 13 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, cleavage, official_alternate_costume, solo, bare_shoulders, detached_sleeves, rabbit_ears, white_dress, beret, large_breasts, looking_at_viewer, long_sleeves, strapless_dress, closed_mouth, hair_ribbon, puffy_sleeves, choker, mismatched_legwear, white_hair, pantyhose, simple_background, teardrop, white_background | | 5 | 59 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, official_alternate_costume, solo, blush, mini_hat, long_sleeves, looking_at_viewer, black_apron, checkered_clothes, hair_ribbon, tilted_headwear, white_background, bowtie, black_cat, animal, white_dress, white_shirt, closed_mouth, simple_background, aged_down, frilled_apron, black_hairband, purple_headwear, black_bow, white_hair | | 6 | 14 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, blush, cleavage, looking_at_viewer, solo, navel, collarbone, large_breasts, teardrop, bare_shoulders, stomach, sidelocks, blue_sky, closed_mouth, day, outdoors, halterneck, ocean, side-tie_bikini_bottom, thighs, beach, black_bikini, cloud, cowboy_shot, front-tie_bikini_top, medium_breasts, water, wet, white_background, jewelry, simple_background | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, closed_mouth, looking_at_viewer, simple_background, solo, white_background, blue_bow, braid, collared_shirt, plaid_skirt, pleated_skirt, school_uniform, white_shirt, blush, long_sleeves, medium_breasts, white_skirt, black_bow, black_jacket, blazer, hair_bow, pantyhose, school_bag, short_sleeves | | 8 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, cleavage, blush, looking_at_viewer, solo, navel, closed_mouth, collarbone, bare_shoulders, white_bra, large_breasts, medium_breasts, white_panties, cowboy_shot, long_sleeves, sitting, stomach, teardrop, white_background, bow, open_clothes, simple_background, thighhighs, underwear_only | | 9 | 8 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, bare_shoulders, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, solo, blush, pantyhose, black_leotard, cleavage, large_breasts, wrist_cuffs, collarbone, detached_collar, hairband, simple_background, strapless_leotard, white_background, closed_mouth, rabbit_tail, teardrop, open_mouth | | 10 | 5 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, floral_print, looking_at_viewer, obi, solo, blush, holding, long_sleeves, alternate_hairstyle, blue_kimono, closed_mouth, hair_flower, print_kimono, sidelocks, smile, yukata, aerial_fireworks, bagged_fish, blue_flower, blurry_background, braid, from_side, goldfish, new_year, outdoors, purple_kimono, signature, single_hair_bun, sitting, upper_body, wide_sleeves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | h&k_hk416 | jacket | solo | assault_rifle | beret | holding_gun | white_gloves | looking_at_viewer | black_thighhighs | long_sleeves | black_headwear | pleated_skirt | heckler_&_koch | plaid_skirt | teardrop | medium_breasts | trigger_discipline | bag | blush | white_hair | white_background | simple_background | closed_mouth | black_skirt | zettai_ryouiki | upper_body | clothes_writing | bare_shoulders | black_gloves | official_alternate_costume | strapless_dress | cleavage | collarbone | layered_dress | white_hairband | flower | large_breasts | black_choker | earrings | hair_ribbon | purple_dress | smile | multi-tied_hair | skirt_hold | bare_arms | detached_sleeves | rabbit_ears | white_dress | puffy_sleeves | choker | mismatched_legwear | pantyhose | mini_hat | black_apron | checkered_clothes | tilted_headwear | bowtie | black_cat | animal | white_shirt | aged_down | frilled_apron | black_hairband | purple_headwear | black_bow | navel | stomach | sidelocks | blue_sky | day | outdoors | halterneck | ocean | side-tie_bikini_bottom | thighs | beach | black_bikini | cloud | cowboy_shot | front-tie_bikini_top | water | wet | jewelry | blue_bow | braid | collared_shirt | school_uniform | white_skirt | black_jacket | blazer | hair_bow | school_bag | short_sleeves | white_bra | white_panties | sitting | bow | open_clothes | thighhighs | underwear_only | fake_animal_ears | playboy_bunny | black_leotard | wrist_cuffs | detached_collar | hairband | strapless_leotard | rabbit_tail | open_mouth | floral_print | obi | holding | alternate_hairstyle | blue_kimono | hair_flower | print_kimono | yukata | aerial_fireworks | bagged_fish | blue_flower | blurry_background | from_side | goldfish | new_year | purple_kimono | signature | single_hair_bun | wide_sleeves | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:------------|:---------|:-------|:----------------|:--------|:--------------|:---------------|:--------------------|:-------------------|:---------------|:-----------------|:----------------|:-----------------|:--------------|:-----------|:-----------------|:---------------------|:------|:--------|:-------------|:-------------------|:--------------------|:---------------|:--------------|:-----------------|:-------------|:------------------|:-----------------|:---------------|:-----------------------------|:------------------|:-----------|:-------------|:----------------|:-----------------|:---------|:----------------|:---------------|:-----------|:--------------|:---------------|:--------|:------------------|:-------------|:------------|:-------------------|:--------------|:--------------|:----------------|:---------|:---------------------|:------------|:-----------|:--------------|:--------------------|:------------------|:---------|:------------|:---------|:--------------|:------------|:----------------|:-----------------|:------------------|:------------|:--------|:----------|:------------|:-----------|:------|:-----------|:-------------|:--------|:-------------------------|:---------|:--------|:---------------|:--------|:--------------|:-----------------------|:--------|:------|:----------|:-----------|:--------|:-----------------|:-----------------|:--------------|:---------------|:---------|:-----------|:-------------|:----------------|:------------|:----------------|:----------|:------|:---------------|:-------------|:-----------------|:-------------------|:----------------|:----------------|:--------------|:------------------|:-----------|:--------------------|:--------------|:-------------|:---------------|:------|:----------|:----------------------|:--------------|:--------------|:---------------|:---------|:-------------------|:--------------|:--------------|:--------------------|:------------|:-----------|:-----------|:----------------|:------------|:------------------|:---------------| | 0 | 49 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | | X | | X | X | X | X | X | X | | X | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | | X | | X | X | | X | X | | | | X | X | | | X | X | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 16 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | | | | | X | X | | | | | | X | X | | | X | | X | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 13 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | | X | | | X | | X | | | | | X | | | | X | X | X | X | X | | | | | X | | X | X | X | | | | | X | | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 59 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 14 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | | | | | X | | | | | | | X | X | | | X | | X | X | X | | | | | X | | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 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 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | X | | | | | X | | X | | | | | X | X | | | X | | X | X | X | | | | | X | | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 8 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | | | | | X | | | | | | | X | | | | X | | X | X | X | | | | | X | | | | X | X | | | | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 10 | 5 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | | | X | | | | | X | | X | | | | | | | | | X | | | | X | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

reza-alipour/muse-landmark-1500

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: mask dtype: image - name: caption dtype: string - name: caption_fre dtype: string - name: caption_deu dtype: string - name: caption_ita dtype: string - name: caption_spa dtype: string - name: generated_mask dtype: image splits: - name: train num_bytes: 570015596.25 num_examples: 1498 download_size: 548973105 dataset_size: 570015596.25 configs: - config_name: default data_files: - split: train path: data/train-* ---

Leon-LLM/Leon-Chess-Dataset-270k-All-Moves-BOS

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 85112470 num_examples: 270052 download_size: 6507602 dataset_size: 85112470 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Leon-Chess-Dataset-270k-All-Moves-BOS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Brizape/Variome_ibo

--- dataset_info: features: - name: id dtype: string - name: token sequence: string - name: ner_tags sequence: int64 splits: - name: train num_bytes: 5447859.708333333 num_examples: 76 - name: validation num_bytes: 1433647.2916666667 num_examples: 20 - name: test num_bytes: 1220147 num_examples: 24 download_size: 691714 dataset_size: 8101654.0 --- # Dataset Card for "Variome_ibo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shidowake/FreedomIntelligence_alpaca-gpt4-japanese_subset_split_0

--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 4863217.322740098 num_examples: 4997 download_size: 2532014 dataset_size: 4863217.322740098 configs: - config_name: default data_files: - split: train path: data/train-* ---

adamjweintraut/loaf

--- dataset_info: features: - name: midi_id dtype: string - name: song_title dtype: string - name: lyrics dtype: string - name: genre dtype: string - name: lyric_summary_bart dtype: string - name: lyric_summary_bartv2 dtype: string - name: topic dtype: string - name: orig_syl_tokens dtype: string - name: orig_note_onsets_tokens dtype: string - name: orig_note_dur_tokens dtype: string - name: orig_note_pitch_tokens dtype: string - name: orig_note_vel_tokens dtype: string - name: orig_note_stress_tokens dtype: string - name: plan_orig dtype: string - name: lines sequence: string - name: kws_per_line sequence: sequence: string - name: plan_target dtype: string - name: lyrics_target dtype: string splits: - name: train num_bytes: 123531832 num_examples: 4489 - name: test num_bytes: 15590559 num_examples: 571 - name: valid num_bytes: 16578892 num_examples: 622 download_size: 36541760 dataset_size: 155701283 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

CyberHarem/wakana_shiki_lovelivesuperstar

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of wakana_shiki/若菜四季/와카나시키 (Love Live! Superstar!!) This is the dataset of wakana_shiki/若菜四季/와카나시키 (Love Live! Superstar!!), containing 239 images and their tags. The core tags of this character are `blue_hair, short_hair, bangs, hair_between_eyes, earrings, ribbon, breasts, red_ribbon, neck_ribbon, orange_eyes, red_eyes`, 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 | 239 | 310.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 239 | 157.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 567 | 353.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 239 | 266.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 567 | 544.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_shiki_lovelivesuperstar/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/wakana_shiki_lovelivesuperstar', 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 | 31 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, yuigaoka_school_uniform, looking_at_viewer, white_shirt, pinafore_dress, blue_jacket, grey_dress, collared_shirt, long_sleeves, white_background, open_jacket, blush, simple_background, upper_body, jewelry, parted_lips | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 2girls, solo_focus, looking_at_viewer, smile, jacket, jewelry, upper_body, birthday, blonde_hair, blush, red_hair, yuigaoka_school_uniform | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, looking_at_viewer, navel, solo, blush, cleavage, collarbone, large_breasts, hair_flower, necklace, open_mouth, brown_eyes, side-tie_bikini_bottom, sitting, smile, thighs | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, looking_at_viewer, midriff, navel, tied_shirt, blue_shirt, blush, medium_breasts, short_sleeves, shorts, solo, arms_up, open_mouth, parted_lips, simple_background, stud_earrings, sweat, white_background, white_pants | | 4 | 7 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1boy, 1girl, blush, hetero, penis, solo_focus, jewelry, navel, nipples, pov, pussy, sex, spread_legs, sweat, vaginal, completely_nude, crossed_bangs, looking_at_viewer, mosaic_censoring, open_mouth, dark-skinned_male, on_back, collarbone, large_breasts, missionary, motion_lines, stomach, upper_teeth_only | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | yuigaoka_school_uniform | looking_at_viewer | white_shirt | pinafore_dress | blue_jacket | grey_dress | collared_shirt | long_sleeves | white_background | open_jacket | blush | simple_background | upper_body | jewelry | parted_lips | 2girls | solo_focus | smile | jacket | birthday | blonde_hair | red_hair | navel | cleavage | collarbone | large_breasts | hair_flower | necklace | open_mouth | brown_eyes | side-tie_bikini_bottom | sitting | thighs | midriff | tied_shirt | blue_shirt | medium_breasts | short_sleeves | shorts | arms_up | stud_earrings | sweat | white_pants | 1boy | hetero | penis | nipples | pov | pussy | sex | spread_legs | vaginal | completely_nude | crossed_bangs | mosaic_censoring | dark-skinned_male | on_back | missionary | motion_lines | stomach | upper_teeth_only | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------------|:--------------------|:--------------|:-----------------|:--------------|:-------------|:-----------------|:---------------|:-------------------|:--------------|:--------|:--------------------|:-------------|:----------|:--------------|:---------|:-------------|:--------|:---------|:-----------|:--------------|:-----------|:--------|:-----------|:-------------|:----------------|:--------------|:-----------|:-------------|:-------------|:-------------------------|:----------|:---------|:----------|:-------------|:-------------|:-----------------|:----------------|:---------|:----------|:----------------|:--------|:--------------|:-------|:---------|:--------|:----------|:------|:--------|:------|:--------------|:----------|:------------------|:----------------|:-------------------|:--------------------|:----------|:-------------|:---------------|:----------|:-------------------| | 0 | 31 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | | | X | X | | | | | | | | | X | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | | | | | | | | | X | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | | | | | | | X | | X | X | | | X | | | | | | | | X | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 4 | 7 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | | | | | | | | | X | | | X | | | X | | | | | | X | | X | X | | | X | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

RiverTest/Testmtg

--- license: apache-2.0 splits: - name: train description: The training dataset (train_Mtg_Dataset.parquet) is used to train the model. - name: test description: The test dataset (test_Mtg_Dataset.parquet) is used to evaluate the model performance on unseen data. ---

SwarmSageGuru/Cifar100

--- license: mit ---

zolak/twitter_dataset_78_1713076263

--- 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: 2661853 num_examples: 6509 download_size: 1344594 dataset_size: 2661853 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/beidou_genshin

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of beidou/北斗/北斗 (Genshin Impact) This is the dataset of beidou/北斗/北斗 (Genshin Impact), containing 500 images and their tags. The core tags of this character are `long_hair, eyepatch, breasts, red_eyes, brown_hair, hair_ornament, hair_over_one_eye, large_breasts, earrings`, 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 | 932.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/beidou_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 784.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/beidou_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1284 | 1.47 GiB | [Download](https://huggingface.co/datasets/CyberHarem/beidou_genshin/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/beidou_genshin', 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 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, fur_trim, hair_stick, hairpin, jewelry, solo, upper_body, chinese_clothes, one_eye_covered, smile, cleavage, dress, looking_at_viewer, black_gloves, fingerless_gloves, parted_lips, simple_background, white_background, capelet | | 1 | 30 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_gloves, hairpin, solo, chinese_clothes, fingerless_gloves, hair_stick, fur_trim, one_eye_covered, looking_at_viewer, holding_sword, cleavage, smile, thighhighs, jewelry, red_dress, red_capelet, pelvic_curtain, vision_(genshin_impact), boots, greatsword | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_gloves, cleavage, dress, fur_trim, hair_stick, hairpin, looking_at_viewer, one_eye_covered, pelvic_curtain, smile, solo, fingerless_gloves, red_capelet, simple_background, thighs, white_background, chinese_clothes, jewelry, black_thighhighs, boots, vision_(genshin_impact) | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, black_bikini, cleavage, looking_at_viewer, navel, solo, collarbone, hairpin, stomach, barefoot, blush, hair_stick, thighs, water, abs, cameltoe, feet, grin, jewelry, muscular_female, ocean, one_eye_covered, toned, wet, white_background | | 4 | 17 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, hair_stick, hairpin, one_eye_covered, erection, jewelry, navel, futanari, large_penis, looking_at_viewer, nipples, testicles, outdoors, sweat, uncensored, blue_sky, teeth, thighs, veiny_penis, ejaculation, grin, huge_penis, completely_nude, day, huge_breasts, projectile_cum, black_hair, cloud, stomach, 1boy, black_thighhighs, solo_focus | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | fur_trim | hair_stick | hairpin | jewelry | solo | upper_body | chinese_clothes | one_eye_covered | smile | cleavage | dress | looking_at_viewer | black_gloves | fingerless_gloves | parted_lips | simple_background | white_background | capelet | holding_sword | thighhighs | red_dress | red_capelet | pelvic_curtain | vision_(genshin_impact) | boots | greatsword | thighs | black_thighhighs | bare_shoulders | black_bikini | navel | collarbone | stomach | barefoot | blush | water | abs | cameltoe | feet | grin | muscular_female | ocean | toned | wet | erection | futanari | large_penis | nipples | testicles | outdoors | sweat | uncensored | blue_sky | teeth | veiny_penis | ejaculation | huge_penis | completely_nude | day | huge_breasts | projectile_cum | black_hair | cloud | 1boy | solo_focus | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-------------|:----------|:----------|:-------|:-------------|:------------------|:------------------|:--------|:-----------|:--------|:--------------------|:---------------|:--------------------|:--------------|:--------------------|:-------------------|:----------|:----------------|:-------------|:------------|:--------------|:-----------------|:--------------------------|:--------|:-------------|:---------|:-------------------|:-----------------|:---------------|:--------|:-------------|:----------|:-----------|:--------|:--------|:------|:-----------|:-------|:-------|:------------------|:--------|:--------|:------|:-----------|:-----------|:--------------|:----------|:------------|:-----------|:--------|:-------------|:-----------|:--------|:--------------|:--------------|:-------------|:------------------|:------|:---------------|:-----------------|:-------------|:--------|:-------|:-------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 30 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | X | X | X | X | | X | X | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | | X | X | | | | | X | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 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 | | | | | | | | | | | | | | | | | | | | | | | 4 | 17 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | X | X | | | | X | | | | X | | | | | | | | | | | | | | | X | X | | | X | | X | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

jaiganesan/real_splitted_image

--- dataset_info: features: - name: image dtype: image - name: text sequence: string splits: - name: train num_bytes: 131840372.0 num_examples: 1000 download_size: 131699741 dataset_size: 131840372.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

pluseen/amore_231101

--- license: apache-2.0 --- <https://civitai.com/models/54867?modelVersionId=207286>

CyberHarem/onitsuka_natsumi_lovelivesuperstar

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of onitsuka_natsumi/鬼塚夏美 (Love Live! Superstar!!) This is the dataset of onitsuka_natsumi/鬼塚夏美 (Love Live! Superstar!!), containing 231 images and their tags. The core tags of this character are `blonde_hair, multicolored_hair, gradient_hair, pink_hair, long_hair, braid, twin_braids, pink_eyes, hair_ornament, hair_flower, bangs, red_eyes, 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 | 231 | 302.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 231 | 155.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 552 | 347.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 231 | 262.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 552 | 535.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/onitsuka_natsumi_lovelivesuperstar/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/onitsuka_natsumi_lovelivesuperstar', 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 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, flower, looking_at_viewer, neck_ribbon, red_ribbon, solo, yuigaoka_school_uniform, blue_jacket, blush, grey_dress, long_sleeves, white_background, collared_shirt, grin, one_eye_closed, open_jacket, pinafore_dress, simple_background, white_shirt | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, red_ribbon, short_sleeves, solo, white_shirt, yuigaoka_school_uniform, neck_ribbon, pinafore_dress, upper_body, flower, summer_uniform, white_background, collared_shirt, blush, grin, one_eye_closed, simple_background | | 2 | 10 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, looking_at_viewer, solo, yuigaoka_school_uniform, flower, smile, dated, happy_birthday, blush, english_text, jacket, character_name, dress, pink_background, upper_body, signature | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, looking_at_viewer, sunglasses, bikini_skirt, cleavage, heart-shaped_eyewear, smile, solo, white_bikini, blush, bracelet, flower, large_breasts, navel, necklace, simple_background, white_background, eyewear_on_head, frills, pink-tinted_eyewear, tongue_out | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, hetero, nipples, solo_focus, large_breasts, pussy, sweat, open_mouth, spread_legs, vaginal, flower, 1boy, bar_censor, cum, navel, breast_grab, gangbang, grabbing, handjob, missionary, multiple_boys, multiple_penises, nude, on_back, panties | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | flower | looking_at_viewer | neck_ribbon | red_ribbon | solo | yuigaoka_school_uniform | blue_jacket | blush | grey_dress | long_sleeves | white_background | collared_shirt | grin | one_eye_closed | open_jacket | pinafore_dress | simple_background | white_shirt | short_sleeves | upper_body | summer_uniform | smile | dated | happy_birthday | english_text | jacket | character_name | dress | pink_background | signature | sunglasses | bikini_skirt | cleavage | heart-shaped_eyewear | white_bikini | bracelet | large_breasts | navel | necklace | eyewear_on_head | frills | pink-tinted_eyewear | tongue_out | hetero | nipples | solo_focus | pussy | sweat | open_mouth | spread_legs | vaginal | 1boy | bar_censor | cum | breast_grab | gangbang | grabbing | handjob | missionary | multiple_boys | multiple_penises | nude | on_back | panties | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------------------|:--------------|:-------------|:-------|:--------------------------|:--------------|:--------|:-------------|:---------------|:-------------------|:-----------------|:-------|:-----------------|:--------------|:-----------------|:--------------------|:--------------|:----------------|:-------------|:-----------------|:--------|:--------|:-----------------|:---------------|:---------|:-----------------|:--------|:------------------|:------------|:-------------|:---------------|:-----------|:-----------------------|:---------------|:-----------|:----------------|:--------|:-----------|:------------------|:---------|:----------------------|:-------------|:---------|:----------|:-------------|:--------|:--------|:-------------|:--------------|:----------|:-------|:-------------|:------|:--------------|:-----------|:-----------|:----------|:-------------|:----------------|:-------------------|:-------|:----------|:----------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | | X | | | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 10 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | | X | X | | X | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | | X | | | X | | | X | | | | | | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 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 |

madrylab/imagenet-star

--- license: mit ---

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_112

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1314464156.0 num_examples: 258143 download_size: 1341360304 dataset_size: 1314464156.0 --- # Dataset Card for "chunk_112" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Aditya685/riddles_improved

--- dataset_info: features: - name: number dtype: int64 - name: messages sequence: string splits: - name: train num_bytes: 895763 num_examples: 669 download_size: 478793 dataset_size: 895763 configs: - config_name: default data_files: - split: train path: data/train-* ---

tr416/dataset_20231006_202418

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 762696.0 num_examples: 297 - name: test num_bytes: 7704.0 num_examples: 3 download_size: 73698 dataset_size: 770400.0 --- # Dataset Card for "dataset_20231006_202418" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BangumiBase/greatpretender

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Great Pretender This is the image base of bangumi Great Pretender, we detected 50 characters, 3820 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 178 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 20 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 647 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 471 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 86 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 79 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 80 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 100 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 110 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 116 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 48 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 35 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 58 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 23 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 30 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 61 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 33 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 59 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 132 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 24 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 9 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 44 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 25 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 42 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 25 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 25 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 83 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 14 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 265 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 16 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 16 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 24 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 16 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 22 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 38 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 37 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 7 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | N/A | | 37 | 18 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 40 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 15 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 281 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 21 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | 42 | 8 | [Download](42/dataset.zip) | ![preview 1](42/preview_1.png) | ![preview 2](42/preview_2.png) | ![preview 3](42/preview_3.png) | ![preview 4](42/preview_4.png) | ![preview 5](42/preview_5.png) | ![preview 6](42/preview_6.png) | ![preview 7](42/preview_7.png) | ![preview 8](42/preview_8.png) | | 43 | 11 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 10 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 18 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | ![preview 6](45/preview_6.png) | ![preview 7](45/preview_7.png) | ![preview 8](45/preview_8.png) | | 46 | 98 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | ![preview 8](46/preview_8.png) | | 47 | 76 | [Download](47/dataset.zip) | ![preview 1](47/preview_1.png) | ![preview 2](47/preview_2.png) | ![preview 3](47/preview_3.png) | ![preview 4](47/preview_4.png) | ![preview 5](47/preview_5.png) | ![preview 6](47/preview_6.png) | ![preview 7](47/preview_7.png) | ![preview 8](47/preview_8.png) | | 48 | 8 | [Download](48/dataset.zip) | ![preview 1](48/preview_1.png) | ![preview 2](48/preview_2.png) | ![preview 3](48/preview_3.png) | ![preview 4](48/preview_4.png) | ![preview 5](48/preview_5.png) | ![preview 6](48/preview_6.png) | ![preview 7](48/preview_7.png) | ![preview 8](48/preview_8.png) | | noise | 118 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

jaimin/Image

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1055814.0 num_examples: 3 download_size: 1058157 dataset_size: 1055814.0 --- # Dataset Card for "Image" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)