davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
liuyanchen1015/MULTI_VALUE_rte_a_ing	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 606461 num_examples: 1438 - name: train num_bytes: 540657 num_examples: 1284 download_size: 752975 dataset_size: 1147118 --- # Dataset Card for "MULTI_VALUE_rte_a_ing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nikutka/L1_poleval_korpus_wzorcowy_train	--- dataset_info: features: - name: content dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 20564 num_examples: 253 download_size: 15381 dataset_size: 20564 --- # Dataset Card for "L1_poleval_korpus_wzorcowy_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yangfu2/test	--- license: mit ---
Daniil-plotnikov/musicdataset	--- tags: - music --- This dataset is a text file with music in ABC format. A compositions is separated from another by two '###'
maximuslee07/raqna1.4k	--- license: llama2 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2507625 num_examples: 1581 download_size: 1451367 dataset_size: 2507625 configs: - config_name: default data_files: - split: train path: data/train-* ---
aagoluoglu/AI_HW3_frames	--- dataset_info: features: - name: video_id dtype: string - name: frame_num dtype: int64 - name: frame_encoded_base64 dtype: string - name: timestamp dtype: float64 splits: - name: train num_bytes: 563645985 num_examples: 344 download_size: 351336726 dataset_size: 563645985 configs: - config_name: default data_files: - split: train path: data/train-* ---
stoddur/medication_chat_4	--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 splits: - name: train num_bytes: 166152928.0 num_examples: 107612 download_size: 2515339 dataset_size: 166152928.0 --- # Dataset Card for "medication_chat_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
distilled-from-one-sec-cv12/chunk_247	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 813139740 num_examples: 158445 download_size: 827010115 dataset_size: 813139740 --- # Dataset Card for "chunk_247" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yaodi/libai	--- license: mit ---
gaizerick/gaizeric	--- license: openrail ---
tj-solergibert/SRV-T5-Europarl-mt-es	--- dataset_info: features: - name: source_text dtype: string - name: dest_text dtype: string - name: dest_lang dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 662085855 num_examples: 523542 - name: valid num_bytes: 91746842 num_examples: 71101 - name: test num_bytes: 95204696 num_examples: 73782 download_size: 275095415 dataset_size: 849037393 --- # Dataset Card for "SRV-T5-Europarl-mt-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2	--- pretty_name: Evaluation run of ZhangShenao/0.001_idpo_declr_4iters_iter_2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ZhangShenao/0.001_idpo_declr_4iters_iter_2](https://huggingface.co/ZhangShenao/0.001_idpo_declr_4iters_iter_2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-08T10:58:14.665887](https://huggingface.co/datasets/open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2/blob/main/results_2024-04-08T10-58-14.665887.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\": 0.6089111130070912,\n\ \ \"acc_stderr\": 0.033058238196911824,\n \"acc_norm\": 0.6153672426330791,\n\ \ \"acc_norm_stderr\": 0.033748541418571455,\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494884,\n \"mc2\": 0.48184218879604507,\n\ \ \"mc2_stderr\": 0.015696974795587824\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946709,\n\ \ \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.01415063143511173\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6548496315475005,\n\ \ \"acc_stderr\": 0.004744456628455121,\n \"acc_norm\": 0.8481378211511651,\n\ \ \"acc_norm_stderr\": 0.0035815378475817974\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\ \ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5838150289017341,\n\ \ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\ \ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4803921568627451,\n \"acc_stderr\": 0.04971358884367405,\n\ \ \"acc_norm\": 0.4803921568627451,\n \"acc_norm_stderr\": 0.04971358884367405\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4312169312169312,\n \"acc_stderr\": 0.025506481698138208,\n \"\ acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.025506481698138208\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.373015873015873,\n\ \ \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n\ \ \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7290322580645161,\n\ \ \"acc_stderr\": 0.025284416114900152,\n \"acc_norm\": 0.7290322580645161,\n\ \ \"acc_norm_stderr\": 0.025284416114900152\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\ : 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n\ \ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5538461538461539,\n \"acc_stderr\": 0.02520357177302833,\n \ \ \"acc_norm\": 0.5538461538461539,\n \"acc_norm_stderr\": 0.02520357177302833\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066475,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066475\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7853211009174312,\n \"acc_stderr\": 0.01760430414925648,\n \"\ acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.01760430414925648\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"\ acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159263,\n \ \ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159263\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n\ \ \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.043300437496507437,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.043300437496507437\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8058748403575989,\n\ \ \"acc_stderr\": 0.01414397027665757,\n \"acc_norm\": 0.8058748403575989,\n\ \ \"acc_norm_stderr\": 0.01414397027665757\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3229050279329609,\n\ \ \"acc_stderr\": 0.015638440380241488,\n \"acc_norm\": 0.3229050279329609,\n\ \ \"acc_norm_stderr\": 0.015638440380241488\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6633986928104575,\n \"acc_stderr\": 0.02705797462449438,\n\ \ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.02705797462449438\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6790123456790124,\n \"acc_stderr\": 0.025976566010862737,\n\ \ \"acc_norm\": 0.6790123456790124,\n \"acc_norm_stderr\": 0.025976566010862737\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4380704041720991,\n\ \ \"acc_stderr\": 0.012671902782567654,\n \"acc_norm\": 0.4380704041720991,\n\ \ \"acc_norm_stderr\": 0.012671902782567654\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.02916312857067073,\n\ \ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.02916312857067073\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6323529411764706,\n \"acc_stderr\": 0.019506291693954854,\n \ \ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.019506291693954854\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.046313813194254656,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.046313813194254656\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484378,\n\ \ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484378\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \ \ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322626\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494884,\n \"mc2\": 0.48184218879604507,\n\ \ \"mc2_stderr\": 0.015696974795587824\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7758484609313339,\n \"acc_stderr\": 0.011720400740774102\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.27824109173616374,\n \ \ \"acc_stderr\": 0.012343803671422673\n }\n}\n```" repo_url: https://huggingface.co/ZhangShenao/0.001_idpo_declr_4iters_iter_2 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_08T10_58_14.665887 path: - '/details_harness\|arc:challenge\|25_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-08T10-58-14.665887.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|gsm8k\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hellaswag\|10_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T10-58-14.665887.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-08T10-58-14.665887.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '/details_harness\|winogrande\|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-08T10-58-14.665887.parquet' - config_name: results data_files: - split: 2024_04_08T10_58_14.665887 path: - results_2024-04-08T10-58-14.665887.parquet - split: latest path: - results_2024-04-08T10-58-14.665887.parquet --- # Dataset Card for Evaluation run of ZhangShenao/0.001_idpo_declr_4iters_iter_2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ZhangShenao/0.001_idpo_declr_4iters_iter_2](https://huggingface.co/ZhangShenao/0.001_idpo_declr_4iters_iter_2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-08T10:58:14.665887](https://huggingface.co/datasets/open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2/blob/main/results_2024-04-08T10-58-14.665887.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": 0.6089111130070912, "acc_stderr": 0.033058238196911824, "acc_norm": 0.6153672426330791, "acc_norm_stderr": 0.033748541418571455, "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494884, "mc2": 0.48184218879604507, "mc2_stderr": 0.015696974795587824 }, "harness\|arc:challenge\|25": { "acc": 0.6006825938566553, "acc_stderr": 0.014312094557946709, "acc_norm": 0.6245733788395904, "acc_norm_stderr": 0.01415063143511173 }, "harness\|hellaswag\|10": { "acc": 0.6548496315475005, "acc_stderr": 0.004744456628455121, "acc_norm": 0.8481378211511651, "acc_norm_stderr": 0.0035815378475817974 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5838150289017341, "acc_stderr": 0.03758517775404947, "acc_norm": 0.5838150289017341, "acc_norm_stderr": 0.03758517775404947 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4803921568627451, "acc_stderr": 0.04971358884367405, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.04971358884367405 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4312169312169312, "acc_stderr": 0.025506481698138208, "acc_norm": 0.4312169312169312, "acc_norm_stderr": 0.025506481698138208 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.373015873015873, "acc_stderr": 0.04325506042017086, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.04325506042017086 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7290322580645161, "acc_stderr": 0.025284416114900152, "acc_norm": 0.7290322580645161, "acc_norm_stderr": 0.025284416114900152 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7626262626262627, "acc_stderr": 0.0303137105381989, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.0303137105381989 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548057, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548057 }, 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"acc_norm_stderr": 0.022209309073165612 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8058748403575989, "acc_stderr": 0.01414397027665757, "acc_norm": 0.8058748403575989, "acc_norm_stderr": 0.01414397027665757 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3229050279329609, "acc_stderr": 0.015638440380241488, "acc_norm": 0.3229050279329609, "acc_norm_stderr": 0.015638440380241488 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6633986928104575, "acc_stderr": 0.02705797462449438, "acc_norm": 0.6633986928104575, "acc_norm_stderr": 0.02705797462449438 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6790123456790124, "acc_stderr": 0.025976566010862737, "acc_norm": 0.6790123456790124, "acc_norm_stderr": 0.025976566010862737 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4380704041720991, "acc_stderr": 0.012671902782567654, "acc_norm": 0.4380704041720991, "acc_norm_stderr": 0.012671902782567654 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6397058823529411, "acc_stderr": 0.02916312857067073, "acc_norm": 0.6397058823529411, "acc_norm_stderr": 0.02916312857067073 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6323529411764706, "acc_stderr": 0.019506291693954854, "acc_norm": 0.6323529411764706, "acc_norm_stderr": 0.019506291693954854 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6272727272727273, "acc_stderr": 0.046313813194254656, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.046313813194254656 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.689795918367347, "acc_stderr": 0.029613459872484378, "acc_norm": 0.689795918367347, "acc_norm_stderr": 0.029613459872484378 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368036, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368036 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494884, "mc2": 0.48184218879604507, "mc2_stderr": 0.015696974795587824 }, "harness\|winogrande\|5": { "acc": 0.7758484609313339, "acc_stderr": 0.011720400740774102 }, "harness\|gsm8k\|5": { "acc": 0.27824109173616374, "acc_stderr": 0.012343803671422673 } } ``` ## 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]
fathyshalab/reklamation24_schoenheit-wellness-full	--- dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction list: - name: label dtype: string - name: score dtype: float64 - name: prediction_agent dtype: string - name: annotation dtype: string - name: annotation_agent dtype: string - name: vectors struct: - name: mini-lm-sentence-transformers sequence: float64 - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 21984670 num_examples: 4158 download_size: 0 dataset_size: 21984670 --- # Dataset Card for "reklamation24_schoenheit-wellness-full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_79_1713117433	--- 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: 264228 num_examples: 629 download_size: 140982 dataset_size: 264228 configs: - config_name: default data_files: - split: train path: data/train-* ---
jxie/wiki_books	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 22230107891 num_examples: 23464781 download_size: 13136529693 dataset_size: 22230107891 --- # Dataset Card for "wiki_books" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mrpc_future_sub_gon	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 38745 num_examples: 143 - name: train num_bytes: 77103 num_examples: 284 - name: validation num_bytes: 11568 num_examples: 41 download_size: 95459 dataset_size: 127416 --- # Dataset Card for "MULTI_VALUE_mrpc_future_sub_gon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yangwang825/sst2-remove-stopwords-n2	--- 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: text dtype: string - name: label dtype: int64 - name: label_text dtype: string splits: - name: train num_bytes: 884164 num_examples: 6920 - name: validation num_bytes: 112712 num_examples: 872 - name: test num_bytes: 218641 num_examples: 1821 download_size: 722219 dataset_size: 1215517 --- # Dataset Card for "sst2-remove-stopwords-n2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sam1120/dropoff-utcustom-EVAL	--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 139241854.0 num_examples: 50 download_size: 40271598 dataset_size: 139241854.0 --- # Dataset Card for "dropoff-utcustom-EVAL" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Circularmachines/batch_indexing_machine_230529_012	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 152482128.0 num_examples: 720 download_size: 152492954 dataset_size: 152482128.0 --- # Dataset Card for "batch_indexing_machine_230529_012" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sarjono/Tester	--- license: cc ---
RomeuForte/jacksparrow	--- license: openrail ---
irds/nyt_wksup_train	--- pretty_name: '`nyt/wksup/train`' viewer: false source_datasets: ['irds/nyt'] task_categories: - text-retrieval --- # Dataset Card for `nyt/wksup/train` The `nyt/wksup/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/nyt#nyt/wksup/train). # Data This dataset provides: - `queries` (i.e., topics); count=1,863,657 - `qrels`: (relevance assessments); count=1,863,657 - For `docs`, use [`irds/nyt`](https://huggingface.co/datasets/irds/nyt) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/nyt_wksup_train', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/nyt_wksup_train', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{MacAvaney2019Wksup, author = {MacAvaney, Sean and Yates, Andrew and Hui, Kai and Frieder, Ophir}, title = {Content-Based Weak Supervision for Ad-Hoc Re-Ranking}, booktitle = {SIGIR}, year = {2019} } @article{Sandhaus2008Nyt, title={The new york times annotated corpus}, author={Sandhaus, Evan}, journal={Linguistic Data Consortium, Philadelphia}, volume={6}, number={12}, pages={e26752}, year={2008} } ```
liuyanchen1015/MULTI_VALUE_mrpc_indef_one	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 199463 num_examples: 701 - name: train num_bytes: 441194 num_examples: 1560 - name: validation num_bytes: 47407 num_examples: 168 download_size: 457651 dataset_size: 688064 --- # Dataset Card for "MULTI_VALUE_mrpc_indef_one" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdapterOcean/med_alpaca_standardized_cluster_34_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 13478218 num_examples: 7361 download_size: 6697396 dataset_size: 13478218 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_34_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kasvii/face-partuv2fulluv-ffhq10-samples	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 14738687.0 num_examples: 10 download_size: 14731902 dataset_size: 14738687.0 --- # Dataset Card for "face-partuv2fulluv-ffhq10-samples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
xfh/ontology_image_audio_2k	--- dataset_info: features: - name: text dtype: string - name: image dtype: image - name: audio dtype: audio - name: tag dtype: string - name: text_id dtype: string splits: - name: train num_examples: 2403 --- From https://github.com/audioset/ontology [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anirhc/sutd_qa_dataset	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 94057.0 num_examples: 200 download_size: 46948 dataset_size: 94057.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
sert121/SpiderSQL	--- license: mit ---
kyriemao/topiocqa	--- license: apache-2.0 ---
suman895/sumanv	--- license: mit ---
CyberHarem/teeny_fireemblem	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of teeny (Fire Emblem) This is the dataset of teeny (Fire Emblem), containing 52 images and their tags. The core tags of this character are `long_hair, twintails, purple_eyes, purple_hair, multi-tied_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 \| 52 \| 43.32 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 52 \| 28.57 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 86 \| 49.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 52 \| 39.83 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 86 \| 66.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/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/teeny_fireemblem', 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 \| 9 \| ![](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, dress, looking_at_viewer, simple_background, solo, smile, white_background, closed_mouth, bare_shoulders, sleeveless, black_gloves, holding_book, jewelry \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| dress \| looking_at_viewer \| simple_background \| solo \| smile \| white_background \| closed_mouth \| bare_shoulders \| sleeveless \| black_gloves \| holding_book \| jewelry \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:--------------------\|:--------------------\|:-------\|:--------\|:-------------------\|:---------------\|:-----------------\|:-------------\|:---------------\|:---------------\|:----------\| \| 0 \| 9 \| ![](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 \|
C-MTEB/CmedqaRetrieval-qrels	--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: qid dtype: string - name: pid dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 595920 num_examples: 7449 download_size: 404005 dataset_size: 595920 --- # Dataset Card for "CmedqaRetrieval-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
howdi2000/may_v2	--- license: unknown ---
Jzuluaga/atcosim_corpus	--- dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: segment_start_time dtype: float32 - name: segment_end_time dtype: float32 - name: duration dtype: float32 splits: - name: test num_bytes: 471628915.76 num_examples: 1901 - name: train num_bytes: 1934757106.88 num_examples: 7638 download_size: 0 dataset_size: 2406386022.6400003 tags: - audio - automatic-speech-recognition - en-atc - en - robust-speech-recognition - noisy-speech-recognition - speech-recognition task_categories: - automatic-speech-recognition language: - en multilinguality: - monolingual --- # Dataset Card for ATCOSIM corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages and Other Details](#languages-and-other-details) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [ATCOSIM homepage](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) - Repository: [GitHub repository (used in research)](https://github.com/idiap/w2v2-air-traffic) - Paper: [The ATCOSIM Corpus of Non-Prompted Clean Air Traffic Control Speech](https://aclanthology.org/L08-1507/) - Paper of this research: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822) ### Dataset Summary The ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database of air traffic control (ATC) operator speech, provided by Graz University of Technology (TUG) and Eurocontrol Experimental Centre (EEC). It consists of ten hours of speech data, which were recorded during ATC real-time simulations using a close-talk headset microphone. The utterances are in English language and pronounced by ten non-native speakers. The database includes orthographic transcriptions and additional information on speakers and recording sessions. It was recorded and annotated by Konrad Hofbauer ([description here](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html)). ### Supported Tasks and Leaderboards - `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [XLS-R-300m](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim). ### Languages and other details The text and the recordings are in English. The participating controllers were all actively employed air traffic controllers and possessed professional experience in the simulated sectors. The six male and four female controllers were of either German or Swiss nationality and had German, Swiss German or Swiss French native tongue. The controllers had agreed to the recording of their voice for the purpose of language analysis as well as for research and development in speech technologies, and were asked to show their normal working behaviour. ## Dataset Structure ### Data Fields - `id (string)`: a string of recording identifier for each example, corresponding to its. - `audio (audio)`: audio data for the given ID - `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc) - `segment_start_time (float32)`: segment start time (normally 0) - `segment_end_time (float32): segment end time - `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time ## Additional Information ### Licensing Information The licensing status of the dataset hinges on the legal status of the [ATCOSIM corpus](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) creators. ### Citation Information Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` Authors of the dataset: ``` @inproceedings{hofbauer-etal-2008-atcosim, title = "The {ATCOSIM} Corpus of Non-Prompted Clean Air Traffic Control Speech", author = "Hofbauer, Konrad and Petrik, Stefan and Hering, Horst", booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)", month = may, year = "2008", address = "Marrakech, Morocco", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/545_paper.pdf", } ```
irds/clueweb12_touche-2020-task-2	--- pretty_name: '`clueweb12/touche-2020-task-2`' viewer: false source_datasets: ['irds/clueweb12'] task_categories: - text-retrieval --- # Dataset Card for `clueweb12/touche-2020-task-2` The `clueweb12/touche-2020-task-2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb12#clueweb12/touche-2020-task-2). # Data This dataset provides: - `queries` (i.e., topics); count=50 - `qrels`: (relevance assessments); count=1,783 - For `docs`, use [`irds/clueweb12`](https://huggingface.co/datasets/irds/clueweb12) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/clueweb12_touche-2020-task-2', 'queries') for record in queries: record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...} qrels = load_dataset('irds/clueweb12_touche-2020-task-2', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Bondarenko2020Touche, address = {Berlin Heidelberg New York}, author = {Alexander Bondarenko and Maik Fr{\"o}be and Meriem Beloucif and Lukas Gienapp and Yamen Ajjour and Alexander Panchenko and Chris Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen}, booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 11th International Conference of the CLEF Association (CLEF 2020)}, doi = {10.1007/978-3-030-58219-7\_26}, editor = {Avi Arampatzis and Evangelos Kanoulas and Theodora Tsikrika and Stefanos Vrochidis and Hideo Joho and Christina Lioma and Carsten Eickhoff and Aur{\'e}lie N{\'e}v{\'e}ol and Linda Cappellato and Nicola Ferro}, month = sep, pages = {384-395}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, site = {Thessaloniki, Greece}, title = {{Overview of Touch{\'e} 2020: Argument Retrieval}}, url = {https://link.springer.com/chapter/10.1007/978-3-030-58219-7_26}, volume = 12260, year = 2020, } @inproceedings{Braunstain2016Support, author = {Liora Braunstain and Oren Kurland and David Carmel and Idan Szpektor and Anna Shtok}, editor = {Nicola Ferro and Fabio Crestani and Marie{-}Francine Moens and Josiane Mothe and Fabrizio Silvestri and Giorgio Maria Di Nunzio and Claudia Hauff and Gianmaria Silvello}, title = {Supporting Human Answers for Advice-Seeking Questions in {CQA} Sites}, booktitle = {Advances in Information Retrieval - 38th European Conference on {IR} Research, {ECIR} 2016, Padua, Italy, March 20-23, 2016. Proceedings}, series = {Lecture Notes in Computer Science}, volume = {9626}, pages = {129--141}, publisher = {Springer}, year = {2016}, doi = {10.1007/978-3-319-30671-1\_10}, } @inproceedings{Rafalak2014Credibility, author = {Maria Rafalak and Katarzyna Abramczuk and Adam Wierzbicki}, editor = {Chin{-}Wan Chung and Andrei Z. Broder and Kyuseok Shim and Torsten Suel}, title = {Incredible: is (almost) all web content trustworthy? analysis of psychological factors related to website credibility evaluation}, booktitle = {23rd International World Wide Web Conference, {WWW} '14, Seoul, Republic of Korea, April 7-11, 2014, Companion Volume}, pages = {1117--1122}, publisher = {{ACM}}, year = {2014}, doi = {10.1145/2567948.2578997}, } ```
CyberHarem/m1_garand_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of m1_garand/M1ガーランド/M1加兰德 (Girls' Frontline) This is the dataset of m1_garand/M1ガーランド/M1加兰德 (Girls' Frontline), containing 73 images and their tags. The core tags of this character are `long_hair, blonde_hair, green_eyes, breasts, large_breasts, hat, hairband, beret, bangs`, 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 \| 73 \| 98.13 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 73 \| 51.83 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 174 \| 112.95 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 73 \| 85.06 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 174 \| 168.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_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/m1_garand_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, blush, hair_flower, cleavage, looking_at_viewer, ponytail, solo, navel, sarong, green_bikini, hibiscus, rifle, smile, black_bikini, collarbone, hairclip, official_alternate_costume, open_mouth, sandals, sling \| \| 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) \| 1boy, 1girl, blush, hetero, solo_focus, sweat, open_mouth, sex, censored, green_bikini, navel, penis, ponytail, arm_grab, cleavage, hair_between_eyes, hair_ornament, looking_at_viewer, male_pubic_hair, missionary, nude, on_back, on_bed, pussy, saliva, spread_legs, vaginal \| \| 2 \| 41 \| ![](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, solo, blue_skirt, looking_at_viewer, black_pantyhose, blush, brown_shirt, pleated_skirt, rifle, jacket, green_necktie, simple_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| hair_flower \| cleavage \| looking_at_viewer \| ponytail \| solo \| navel \| sarong \| green_bikini \| hibiscus \| rifle \| smile \| black_bikini \| collarbone \| hairclip \| official_alternate_costume \| open_mouth \| sandals \| sling \| 1boy \| hetero \| solo_focus \| sweat \| sex \| censored \| penis \| arm_grab \| hair_between_eyes \| hair_ornament \| male_pubic_hair \| missionary \| nude \| on_back \| on_bed \| pussy \| saliva \| spread_legs \| vaginal \| blue_skirt \| black_pantyhose \| brown_shirt \| pleated_skirt \| jacket \| green_necktie \| simple_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:--------------\|:-----------\|:--------------------\|:-----------\|:-------\|:--------\|:---------\|:---------------\|:-----------\|:--------\|:--------\|:---------------\|:-------------\|:-----------\|:-----------------------------\|:-------------\|:----------\|:--------\|:-------\|:---------\|:-------------\|:--------\|:------\|:-----------\|:--------\|:-----------\|:--------------------\|:----------------\|:------------------\|:-------------\|:-------\|:----------\|:---------\|:--------\|:---------\|:--------------\|:----------\|:-------------\|:------------------\|:--------------\|:----------------\|:---------\|:----------------\|:--------------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| 2 \| 41 \| ![](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 \|
liuyanchen1015/MULTI_VALUE_qqp_if_would	--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 597773 num_examples: 2940 - name: test num_bytes: 5891468 num_examples: 29705 - name: train num_bytes: 5380495 num_examples: 26549 download_size: 7271826 dataset_size: 11869736 --- # Dataset Card for "MULTI_VALUE_qqp_if_would" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
beltrewilton/punta-cana-spanish-reviews	--- license: mit task_categories: - text-classification language: - es --- This data set was collected for academic purposes, suitable for some NLP tasks including sentiment analysis.
jccoutojr/renatavoice	--- license: openrail ---
zolak/twitter_dataset_79_1713070286	--- 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: 2570928 num_examples: 6414 download_size: 1287018 dataset_size: 2570928 configs: - config_name: default data_files: - split: train path: data/train-* ---
allenai/ultrafeedback_binarized_cleaned	--- license: mit configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: source dtype: string splits: - name: train_sft num_bytes: 393926052.7984401 num_examples: 60829 - name: test_sft num_bytes: 6230841.363636363 num_examples: 985 - name: train_gen num_bytes: 314344767.49216783 num_examples: 60829 - name: test_gen num_bytes: 4982506.090909091 num_examples: 985 - name: train_prefs num_bytes: 393926052.7984401 num_examples: 60829 - name: test_prefs num_bytes: 12672623.615773508 num_examples: 1964 download_size: 629736515 dataset_size: 1126082844.1593668 --- # Dataset Card for "ultrafeedback_binarized_cleaned" Update 1/12/2023: I've removed examples identified as faulty by Argilla - see [their awesome work](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) for more details. This is a version of the [UltraFeedback binarized dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) but with TruthfulQA prompts removed and source annotations added (so you can filter out samples from different sources yourself if you want!). Please see the [binarized dataset card for more information](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), or the [original UltraFeedback dataset card](https://huggingface.co/datasets/openbmb/UltraFeedback).
andrinho1010/coringa.mp3	--- license: openrail ---
kye/all-pytorch-code	--- license: mit ---
benmccloskey/reviews	--- license: pddl ---
ymoslem/MediaSpeech	--- dataset_info: description: > MediaSpeech is a dataset of Arabic, French, Spanish, and Turkish media speech built with the purpose of testing Automated Speech Recognition (ASR) systems performance. features: - name: audio dtype: audio sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_examples: 10023 configs: - config_name: ar data_files: - split: train path: ar/train-* - config_name: fr data_files: - split: train path: fr/train-* - config_name: es data_files: - split: train path: es/train-* - config_name: tr data_files: - split: train path: tr/train-* license: cc-by-4.0 language: - ar - fr - es - tr pretty_name: MediaSpeech size_categories: - 1K<n<10K tags: - speech task_categories: - automatic-speech-recognition - text-to-speech --- # MediaSpeech MediaSpeech is a dataset of Arabic, French, Spanish, and Turkish media speech built with the purpose of testing Automated Speech Recognition (ASR) systems performance. The dataset contains 10 hours of speech for each language provided. The dataset consists of short speech segments automatically extracted from media videos available on YouTube and manually transcribed, with some pre-processing and post-processing. Baseline models and WAV version of the dataset can be found in this [git repository](https://github.com/NTRLab/MediaSpeech). ## How to load the dataset The dataset has 4 languages: Arabic (`ar`), Spanish (`es`), French (`fr`), and Turkish (`tr`). To load a language portion of the dataset: ``` from datasets import load_dataset downloaded_dataset = load_dataset("ymoslem/MediaSpeech", "ar", split="train") ``` ## Dataset structure The dataset structure is as follows: ``` DatasetDict({ train: Dataset({ features: ['audio', 'sentence'], num_rows: 2505 }) }) ``` ## Citation To cite the dataset, use the following BibTeX entry: ``` @misc{mediaspeech2021, title={MediaSpeech: Multilanguage ASR Benchmark and Dataset}, author={Rostislav Kolobov and Olga Okhapkina and Olga Omelchishina, Andrey Platunov and Roman Bedyakin and Vyacheslav Moshkin and Dmitry Menshikov and Nikolay Mikhaylovskiy}, year={2021}, eprint={2103.16193}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```
huggingartists/bones	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/bones" ## 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: 1.235927 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/564dc935d7c601860b155b359d8ddf9d.1000x1000x1.png')"> </div> </div> <a href="https://huggingface.co/huggingartists/bones"> <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">BONES</div> <a href="https://genius.com/artists/bones"> <div style="text-align: center; font-size: 14px;">@bones</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/bones). ### 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/bones") ``` ## 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\| \|------:\|---------:\|---:\| \|1156\| -\| -\| '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/bones") 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)
rouskinlab/lncRNA	--- license: mit language: - en tags: - chemistry - biology` author: Silvi Rouskin source: data.json date: 2024-03-19-12-30-41 --- # Data types - sequence: 10 datapoints - structure: 10 datapoints # Conversion report Over a total of 10 datapoints, there are: ### OUTPUT - ALL: 10 valid datapoints - INCLUDED: 0 duplicate sequences with different structure / dms / shape ### MODIFIED - 0 multiple sequences with the same reference (renamed reference) ### FILTERED OUT - 0 invalid datapoints (ex: sequence with non-regular characters) - 0 datapoints with bad structures - 0 duplicate sequences with the same structure / dms / shape
baylitoo/hello	--- dataset_info: features: - name: page_hash dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: normalized_bboxes sequence: sequence: int64 - name: labels sequence: int64 - name: original_image dtype: image splits: - name: train num_bytes: 4038206084.848 num_examples: 15009 - name: validation num_bytes: 407608434.304 num_examples: 1607 - name: test num_bytes: 268092681.752 num_examples: 1041 download_size: 1783996007 dataset_size: 4713907200.904 --- # Dataset Card for "hello" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-elementary_mathematics-original-neg-prepend	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_prompt dtype: string splits: - name: test num_bytes: 5179 num_examples: 14 download_size: 7035 dataset_size: 5179 --- # Dataset Card for "mmlu-elementary_mathematics-original-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
prantadi/tokenized_dataset_intent_3600	--- license: apache-2.0 ---
open-llm-leaderboard/details_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup	--- pretty_name: Evaluation run of jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup](https://huggingface.co/jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup)\ \ 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_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-12T08:24:49.664707](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup/blob/main/results_2024-02-12T08-24-49.664707.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\": 0.604711622779494,\n\ \ \"acc_stderr\": 0.032467251718581315,\n \"acc_norm\": 0.6163715674554368,\n\ \ \"acc_norm_stderr\": 0.03334110735529664,\n \"mc1\": 0.2937576499388005,\n\ \ \"mc1_stderr\": 0.015945068581236618,\n \"mc2\": 0.45375623938134657,\n\ \ \"mc2_stderr\": 0.015248519436290428\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520763,\n\ \ \"acc_norm\": 0.6006825938566553,\n \"acc_norm_stderr\": 0.014312094557946704\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6222863971320454,\n\ \ \"acc_stderr\": 0.004838246410786262,\n \"acc_norm\": 0.8218482374029078,\n\ \ \"acc_norm_stderr\": 0.0038185843846355303\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5555555555555556,\n\ \ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n\ \ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\ \ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.03745554791462456,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.03745554791462456\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5780346820809249,\n\ \ \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n\ \ \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.35978835978835977,\n \"acc_stderr\": 0.02471807594412928,\n \"\ acc_norm\": 0.35978835978835977,\n \"acc_norm_stderr\": 0.02471807594412928\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7064516129032258,\n\ \ \"acc_stderr\": 0.025906087021319295,\n \"acc_norm\": 0.7064516129032258,\n\ \ \"acc_norm_stderr\": 0.025906087021319295\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.034953345821629345,\n\ \ \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.034953345821629345\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.0291265228345868,\n \"acc_norm\"\ : 0.7878787878787878,\n \"acc_norm_stderr\": 0.0291265228345868\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.02649905770139744,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.02649905770139744\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6076923076923076,\n \"acc_stderr\": 0.02475600038213095,\n \ \ \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.02475600038213095\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.27037037037037037,\n \"acc_stderr\": 0.02708037281514565,\n \ \ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.02708037281514565\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\ \ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7743119266055046,\n \"acc_stderr\": 0.017923087667803064,\n \"\ acc_norm\": 0.7743119266055046,\n \"acc_norm_stderr\": 0.017923087667803064\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5740740740740741,\n \"acc_stderr\": 0.033723432716530624,\n \"\ acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.033723432716530624\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931048,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931048\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8227848101265823,\n \"acc_stderr\": 0.02485636418450322,\n \ \ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.02485636418450322\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\ \ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\ : 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n \ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n\ \ \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7982120051085568,\n\ \ \"acc_stderr\": 0.014351702181636863,\n \"acc_norm\": 0.7982120051085568,\n\ \ \"acc_norm_stderr\": 0.014351702181636863\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.28938547486033517,\n\ \ \"acc_stderr\": 0.015166544550490298,\n \"acc_norm\": 0.28938547486033517,\n\ \ \"acc_norm_stderr\": 0.015166544550490298\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.026716118380156847,\n\ \ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.026716118380156847\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811032,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811032\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6882716049382716,\n \"acc_stderr\": 0.025773111169630457,\n\ \ \"acc_norm\": 0.6882716049382716,\n \"acc_norm_stderr\": 0.025773111169630457\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4667535853976532,\n\ \ \"acc_stderr\": 0.012741974333897224,\n \"acc_norm\": 0.4667535853976532,\n\ \ \"acc_norm_stderr\": 0.012741974333897224\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6258169934640523,\n \"acc_stderr\": 0.019576953122088837,\n \ \ \"acc_norm\": 0.6258169934640523,\n \"acc_norm_stderr\": 0.019576953122088837\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910508,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910508\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.029393609319879804,\n\ \ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.029393609319879804\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7860696517412935,\n\ \ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n\ \ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.03218093795602357,\n\ \ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.03218093795602357\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2937576499388005,\n\ \ \"mc1_stderr\": 0.015945068581236618,\n \"mc2\": 0.45375623938134657,\n\ \ \"mc2_stderr\": 0.015248519436290428\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233626\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup 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_02_12T08_24_49.664707 path: - '/details_harness\|arc:challenge\|25_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-12T08-24-49.664707.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|gsm8k\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hellaswag\|10_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-12T08-24-49.664707.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-12T08-24-49.664707.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '/details_harness\|winogrande\|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-12T08-24-49.664707.parquet' - config_name: results data_files: - split: 2024_02_12T08_24_49.664707 path: - results_2024-02-12T08-24-49.664707.parquet - split: latest path: - results_2024-02-12T08-24-49.664707.parquet --- # Dataset Card for Evaluation run of jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup](https://huggingface.co/jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup) 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_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-12T08:24:49.664707](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup/blob/main/results_2024-02-12T08-24-49.664707.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": 0.604711622779494, "acc_stderr": 0.032467251718581315, "acc_norm": 0.6163715674554368, "acc_norm_stderr": 0.03334110735529664, "mc1": 0.2937576499388005, "mc1_stderr": 0.015945068581236618, "mc2": 0.45375623938134657, "mc2_stderr": 0.015248519436290428 }, "harness\|arc:challenge\|25": { "acc": 0.5750853242320819, "acc_stderr": 0.014445698968520763, "acc_norm": 0.6006825938566553, "acc_norm_stderr": 0.014312094557946704 }, "harness\|hellaswag\|10": { "acc": 0.6222863971320454, "acc_stderr": 0.004838246410786262, "acc_norm": 0.8218482374029078, "acc_norm_stderr": 0.0038185843846355303 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04292596718256981, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04292596718256981 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001974, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.03745554791462456, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.03745554791462456 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.048108401480826346, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.048108401480826346 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.574468085106383, "acc_stderr": 0.03232146916224468, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224468 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.35978835978835977, "acc_stderr": 0.02471807594412928, "acc_norm": 0.35978835978835977, "acc_norm_stderr": 0.02471807594412928 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949098, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949098 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7064516129032258, "acc_stderr": 0.025906087021319295, "acc_norm": 0.7064516129032258, "acc_norm_stderr": 0.025906087021319295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4433497536945813, "acc_stderr": 0.034953345821629345, "acc_norm": 0.4433497536945813, "acc_norm_stderr": 0.034953345821629345 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8, "acc_stderr": 0.031234752377721175, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721175 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.0291265228345868, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.0291265228345868 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8393782383419689, "acc_stderr": 0.02649905770139744, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.02649905770139744 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6076923076923076, "acc_stderr": 0.02475600038213095, "acc_norm": 0.6076923076923076, "acc_norm_stderr": 0.02475600038213095 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.27037037037037037, "acc_stderr": 0.02708037281514565, "acc_norm": 0.27037037037037037, "acc_norm_stderr": 0.02708037281514565 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6428571428571429, "acc_stderr": 0.031124619309328177, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.031124619309328177 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526732, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526732 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7743119266055046, "acc_stderr": 0.017923087667803064, "acc_norm": 0.7743119266055046, "acc_norm_stderr": 0.017923087667803064 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5740740740740741, "acc_stderr": 0.033723432716530624, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.033723432716530624 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931048, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931048 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8227848101265823, "acc_stderr": 0.02485636418450322, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.02485636418450322 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302872, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302872 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7116564417177914, "acc_stderr": 0.035590395316173425, "acc_norm": 0.7116564417177914, "acc_norm_stderr": 0.035590395316173425 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.41964285714285715, "acc_stderr": 0.046840993210771065, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.046840993210771065 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281376, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281376 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7982120051085568, "acc_stderr": 0.014351702181636863, "acc_norm": 0.7982120051085568, "acc_norm_stderr": 0.014351702181636863 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.28938547486033517, "acc_stderr": 0.015166544550490298, "acc_norm": 0.28938547486033517, "acc_norm_stderr": 0.015166544550490298 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6797385620915033, "acc_stderr": 0.026716118380156847, "acc_norm": 0.6797385620915033, "acc_norm_stderr": 0.026716118380156847 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811032, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811032 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6882716049382716, "acc_stderr": 0.025773111169630457, "acc_norm": 0.6882716049382716, "acc_norm_stderr": 0.025773111169630457 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4667535853976532, "acc_stderr": 0.012741974333897224, "acc_norm": 0.4667535853976532, "acc_norm_stderr": 0.012741974333897224 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6654411764705882, "acc_stderr": 0.0286619962023353, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.0286619962023353 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6258169934640523, "acc_stderr": 0.019576953122088837, "acc_norm": 0.6258169934640523, "acc_norm_stderr": 0.019576953122088837 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910508, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910508 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6979591836734694, "acc_stderr": 0.029393609319879804, "acc_norm": 0.6979591836734694, "acc_norm_stderr": 0.029393609319879804 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7860696517412935, "acc_stderr": 0.02899690969332891, "acc_norm": 0.7860696517412935, "acc_norm_stderr": 0.02899690969332891 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7719298245614035, "acc_stderr": 0.03218093795602357, "acc_norm": 0.7719298245614035, "acc_norm_stderr": 0.03218093795602357 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2937576499388005, "mc1_stderr": 0.015945068581236618, "mc2": 0.45375623938134657, "mc2_stderr": 0.015248519436290428 }, "harness\|winogrande\|5": { "acc": 0.7466456195737964, "acc_stderr": 0.012223754434233626 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]
LinhDuong/mito	--- license: apache-2.0 size_categories: - n<1K tags: - biology dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 259624242.0 num_examples: 165 download_size: 136645365 dataset_size: 259624242.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/tanaka_asuka_soundeuphonium	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tanaka Asuka/田中あすか (Sound! Euphonium) This is the dataset of Tanaka Asuka/田中あすか (Sound! Euphonium), containing 479 images and their tags. The core tags of this character are `black_hair, long_hair, glasses, semi-rimless_eyewear, blue_eyes, red-framed_eyewear, over-rim_eyewear, hair_between_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 \| 479 \| 321.07 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tanaka_asuka_soundeuphonium/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 479 \| 320.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tanaka_asuka_soundeuphonium/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 908 \| 567.21 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tanaka_asuka_soundeuphonium/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/tanaka_asuka_soundeuphonium', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 5 \| ![](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) \| blush, green_neckerchief, kitauji_high_school_uniform, looking_at_viewer, sailor_collar, serafuku, solo_focus, 2girls, skirt, smile, 1girl, blurry \| \| 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) \| 1girl, green_neckerchief, kitauji_high_school_uniform, looking_at_viewer, sailor_collar, serafuku, solo, instrument \| \| 2 \| 26 \| ![](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, blush, kitauji_high_school_uniform, sailor_collar, serafuku, green_neckerchief, looking_at_viewer, solo, smile \| \| 3 \| 9 \| ![](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, blue_sailor_collar, blue_skirt, blush, green_neckerchief, kitauji_high_school_uniform, pleated_skirt, serafuku, short_sleeves, solo, white_shirt, indoors, open_mouth, smile, looking_at_viewer \| \| 4 \| 6 \| ![](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, green_neckerchief, kitauji_high_school_uniform, serafuku, short_sleeves, solo, white_shirt, blue_sailor_collar, upper_body, closed_mouth \| \| 5 \| 17 \| ![](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, black_pantyhose, kitauji_high_school_uniform, pleated_skirt, serafuku, solo, green_neckerchief, smile, long_sleeves, white_sailor_collar, brown_skirt, indoors, looking_at_viewer, open_mouth, chalkboard \| \| 6 \| 9 \| ![](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) \| chair, classroom, green_neckerchief, indoors, instrument, kitauji_high_school_uniform, serafuku, sitting, 1girl, solo, chalkboard, blue_skirt, black_pantyhose, blue_sailor_collar, desk, pleated_skirt, short_sleeves, holding \| \| 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, looking_at_viewer, scarf, snowing, solo, upper_body, coat, smile, winter_clothes, blush, hands_on_own_face, outdoors \| \| 8 \| 5 \| ![](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, anime_coloring, lipstick, necklace, solo, blurry, collarbone, ponytail, shirt, indoors, hair_ornament, looking_at_viewer, sweatdrop \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| blush \| green_neckerchief \| kitauji_high_school_uniform \| looking_at_viewer \| sailor_collar \| serafuku \| solo_focus \| 2girls \| skirt \| smile \| 1girl \| blurry \| solo \| instrument \| blue_sailor_collar \| blue_skirt \| pleated_skirt \| short_sleeves \| white_shirt \| indoors \| open_mouth \| upper_body \| closed_mouth \| black_pantyhose \| long_sleeves \| white_sailor_collar \| brown_skirt \| chalkboard \| chair \| classroom \| sitting \| desk \| holding \| scarf \| snowing \| coat \| winter_clothes \| hands_on_own_face \| outdoors \| anime_coloring \| lipstick \| necklace \| collarbone \| ponytail \| shirt \| hair_ornament \| sweatdrop \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:------------------------------\|:--------------------\|:----------------\|:-----------\|:-------------\|:---------\|:--------\|:--------\|:--------\|:---------\|:-------\|:-------------\|:---------------------\|:-------------\|:----------------\|:----------------\|:--------------\|:----------\|:-------------\|:-------------\|:---------------\|:------------------\|:---------------\|:----------------------\|:--------------\|:-------------\|:--------\|:------------\|:----------\|:-------\|:----------\|:--------\|:----------\|:-------\|:-----------------\|:--------------------\|:-----------\|:-----------------\|:-----------\|:-----------\|:-------------\|:-----------\|:--------\|:----------------\|:------------\| \| 0 \| 5 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 26 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 17 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| 8 \| 5 \| ![](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 \|
psyche/nmt-sample	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: source_language dtype: string - name: target_language dtype: string - name: category dtype: string splits: - name: train num_bytes: 988 num_examples: 3 download_size: 5473 dataset_size: 988 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "nmt-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mrpc_perfect_slam	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 42425 num_examples: 144 - name: train num_bytes: 103515 num_examples: 367 - name: validation num_bytes: 12295 num_examples: 44 download_size: 113577 dataset_size: 158235 --- # Dataset Card for "MULTI_VALUE_mrpc_perfect_slam" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
selenalu/data	--- configs: - config_name: default data_files: - split: train path: '*/.jsonl' --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
aisc-team-d2/healthsearchqa	--- dataset_info: features: - name: id dtype: float64 - name: question dtype: string splits: - name: train num_bytes: 170966 num_examples: 4436 download_size: 79303 dataset_size: 170966 configs: - config_name: default data_files: - split: train path: data/train-* license: unknown task_categories: - question-answering language: - en tags: - medical size_categories: - 1K<n<10K --- # HealthSearchQA Dataset of consumer health questions released by Google for the Med-PaLM paper ([arXiv preprint](https://arxiv.org/abs/2212.13138)). From the [paper](https://www.nature.com/articles/s41586-023-06291-2): We curated our own additional dataset consisting of 3,173 commonly searched consumer questions, referred to as HealthSearchQA. The dataset was curated using seed medical conditions and their associated symptoms. We used the seed data to retrieve publicly-available commonly searched questions generated by a search engine, which were displayed to all users entering the seed terms. We publish the dataset as an open benchmark for answering medical questions from consumers and hope this will be a useful resource for the community, as a dataset reflecting real-world consumer concerns. Format: Question only, free text response, open domain. Size: 3,173. Example question: How serious is atrial fibrillation? Example question: What kind of cough comes with Covid? Example question: Is blood in phlegm serious?
HOXSEC/cs2-maps	--- license: mit ---
clinicalnlplab/medMCQA_test	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: train num_bytes: 2873180 num_examples: 4183 - name: valid num_bytes: 2873180 num_examples: 4183 - name: test num_bytes: 2873180 num_examples: 4183 download_size: 2161257 dataset_size: 8619540 --- # Dataset Card for "medMCQA_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
soban09/myntra-men-formal-shirt	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 85061.0 num_examples: 10 download_size: 82189 dataset_size: 85061.0 --- # Dataset Card for "myntra-men-formal-shirt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
communityai/apt-orion-math-test-v1	--- dataset_info: features: - name: tokens dtype: int64 - name: uid dtype: string - name: source dtype: string - name: system dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 42760980.0 num_examples: 1694 download_size: 18070881 dataset_size: 42760980.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/tapris_sugarbell_chisaki_gabrieldropout	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tapris Sugarbell Chisaki This is the dataset of Tapris Sugarbell Chisaki, containing 75 images and their tags. 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)). \| Name \| Images \| Download \| Description \| \|:----------------\|---------:\|:----------------------------------------\|:-----------------------------------------------------------------------------------------\| \| raw \| 75 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 163 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 224 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 75 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 75 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 75 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 163 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 163 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 148 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 224 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 224 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
jovianzm/Pexels-400k	--- license: mit task_categories: - image-to-text - text-to-image - text-to-video - image-to-video language: - en pretty_name: Pexels-400k size_categories: - 100K<n<1M --- # Pexels 400k Dataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption. Note: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed.
CyberHarem/m1918_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of m1918/M1918/M1918 (Girls' Frontline) This is the dataset of m1918/M1918/M1918 (Girls' Frontline), containing 79 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, sunglasses, ahoge, eyewear_on_head, large_breasts, aviator_sunglasses, green_eyes, very_long_hair, bangs, low-tied_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 \| 79 \| 98.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 79 \| 54.12 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 180 \| 114.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 79 \| 85.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 180 \| 160.99 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/m1918_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/m1918_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 \| 22 \| ![](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, blush, detached_collar, looking_at_viewer, official_alternate_costume, playboy_bunny, bare_shoulders, open_mouth, rabbit_ears, solo, fake_animal_ears, black_leotard, necktie, cleavage, fishnets, wrist_cuffs, white_background, high_heels, holding, machine_gun, black_footwear, full_body, hair_flaps, standing, stuffed_animal, black_pantyhose, simple_background, strapless_leotard \| \| 1 \| 8 \| ![](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, open_mouth, solo, black_dress, blush, cleavage, official_alternate_costume, machine_gun, thighhighs, necklace, black_gloves, full_body, high_heels, looking_at_viewer, bare_shoulders, black_choker, black_footwear, collarbone, covering, formal, torn_clothes \| \| 2 \| 14 \| ![](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, green_necktie, solo, holding_gun, open_mouth, black_skirt, blush, machine_gun, shirt, white_thighhighs, looking_at_viewer, military_uniform, pleated_skirt, simple_background, black_jacket, miniskirt, shoes, white_background, black_footwear, long_sleeves, green_bow, hair_bow, hair_flaps, standing, ass, hair_between_eyes, smile \| \| 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, solo, green_necktie, hair_flaps, pleated_skirt, shirt, smile, black_skirt, blush, hair_between_eyes, looking_at_viewer, open_mouth, blazer, military_uniform, simple_background, white_background, yellow_eyes \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| detached_collar \| looking_at_viewer \| official_alternate_costume \| playboy_bunny \| bare_shoulders \| open_mouth \| rabbit_ears \| solo \| fake_animal_ears \| black_leotard \| necktie \| cleavage \| fishnets \| wrist_cuffs \| white_background \| high_heels \| holding \| machine_gun \| black_footwear \| full_body \| hair_flaps \| standing \| stuffed_animal \| black_pantyhose \| simple_background \| strapless_leotard \| black_dress \| thighhighs \| necklace \| black_gloves \| black_choker \| collarbone \| covering \| formal \| torn_clothes \| green_necktie \| holding_gun \| black_skirt \| shirt \| white_thighhighs \| military_uniform \| pleated_skirt \| black_jacket \| miniskirt \| shoes \| long_sleeves \| green_bow \| hair_bow \| ass \| hair_between_eyes \| smile \| blazer \| yellow_eyes \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:------------------\|:--------------------\|:-----------------------------\|:----------------\|:-----------------\|:-------------\|:--------------\|:-------\|:-------------------\|:----------------\|:----------\|:-----------\|:-----------\|:--------------\|:-------------------\|:-------------\|:----------\|:--------------\|:-----------------\|:------------\|:-------------\|:-----------\|:-----------------\|:------------------\|:--------------------\|:--------------------\|:--------------\|:-------------\|:-----------\|:---------------\|:---------------\|:-------------\|:-----------\|:---------\|:---------------\|:----------------\|:--------------\|:--------------\|:--------\|:-------------------\|:-------------------\|:----------------\|:---------------\|:------------\|:--------\|:---------------\|:------------\|:-----------\|:------\|:--------------------\|:--------\|:---------\|:--------------\| \| 0 \| 22 \| ![](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 \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 8 \| ![](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 \| 14 \| ![](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 \| 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 \|
heliosprime/twitter_dataset_1713052381	--- 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: 15743 num_examples: 35 download_size: 10294 dataset_size: 15743 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713052381" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Codec-SUPERB/beijing_opera_unit	--- configs: - config_name: default data_files: - 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: id dtype: string - name: unit sequence: sequence: int64 splits: - name: academicodec_hifi_16k_320d num_bytes: 1808834 num_examples: 236 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 1808834 num_examples: 236 - name: academicodec_hifi_24k_320d num_bytes: 2707522 num_examples: 236 - name: audiodec_24k_320d num_bytes: 5784962 num_examples: 236 - name: dac_16k num_bytes: 5433794 num_examples: 236 - name: dac_24k num_bytes: 21666818 num_examples: 236 - name: dac_44k num_bytes: 6999890 num_examples: 236 - name: encodec_24k_12bps num_bytes: 10837250 num_examples: 236 - name: encodec_24k_1_5bps num_bytes: 1361378 num_examples: 236 - name: encodec_24k_24bps num_bytes: 21666818 num_examples: 236 - name: encodec_24k_3bps num_bytes: 2715074 num_examples: 236 - name: encodec_24k_6bps num_bytes: 5422466 num_examples: 236 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 7287810 num_examples: 236 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 7287810 num_examples: 236 - name: speech_tokenizer_16k num_bytes: 3625090 num_examples: 236 download_size: 16959778 dataset_size: 164323606 --- # Dataset Card for "beijing_opera_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/50e86b1c	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1336 dataset_size: 182 --- # Dataset Card for "50e86b1c" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
smithr14buckeye/whistlingflagsdataset	--- language: - en pretty_name: Hole and Round Stats ---
anan-2024/twitter_dataset_1712959978	--- 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: 22523 num_examples: 49 download_size: 13744 dataset_size: 22523 configs: - config_name: default data_files: - split: train path: data/train-* ---
tabcoin/test	--- license: openrail ---
d4rk3r/food	--- license: mit ---
saibo/wikinre_catalog	--- dataset_info: features: - name: en_label dtype: string splits: - name: entity num_bytes: 5123074 num_examples: 278843 - name: relation num_bytes: 2887 num_examples: 158 download_size: 4526528 dataset_size: 5125961 language: - en size_categories: - 100K<n<1M --- # Dataset Card for "wikinre_catalog" Catalog for https://huggingface.co/datasets/saibo/wiki-nre This catalog can be used to do constrained generation. All entities and relations can be found in wikidata by searching https://www.wikidata.org/wiki/ [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
friedrichor/PhotoChat	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4582797667.34 num_examples: 8540 download_size: 4857362900 dataset_size: 4582797667.34 --- # Dataset Card for "PhotoChat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Pablao0948/Datena	--- license: openrail ---
mateuzim/LAUANA86	--- license: openrail ---
quocanh34/test_result_large_synthesis_data_tunelm	--- dataset_info: features: - name: id dtype: string - name: pred_str dtype: string - name: test_norm dtype: string splits: - name: train num_bytes: 208389 num_examples: 1299 download_size: 109281 dataset_size: 208389 --- # Dataset Card for "test_result_large_synthesis_data_tunelm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/kawashima_mizuki_idolmastercinderellagirls	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kawashima_mizuki/川島瑞樹 (THE iDOLM@STER: Cinderella Girls) This is the dataset of kawashima_mizuki/川島瑞樹 (THE iDOLM@STER: Cinderella Girls), containing 169 images and their tags. The core tags of this character are `brown_hair, brown_eyes, long_hair, breasts, ponytail`, 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 \| 169 \| 133.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 169 \| 102.57 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 315 \| 182.78 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 169 \| 127.03 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 315 \| 223.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/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/kawashima_mizuki_idolmastercinderellagirls', 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 \| 8 \| ![](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, open_mouth, solo, blush, necklace, smile, large_breasts, looking_at_viewer, bracelet, cleavage \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| open_mouth \| solo \| blush \| necklace \| smile \| large_breasts \| looking_at_viewer \| bracelet \| cleavage \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------------\|:-------\|:--------\|:-----------\|:--------\|:----------------\|:--------------------\|:-----------\|:-----------\| \| 0 \| 8 \| ![](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 \|
crewdon/instructionPairedFormularDataset13k	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 3190559 num_examples: 13655 download_size: 1482698 dataset_size: 3190559 --- # Dataset Card for "instructionPairedFormularDataset13k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-one-sec-cv12/chunk_241	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1453450296 num_examples: 285438 download_size: 1484410260 dataset_size: 1453450296 --- # Dataset Card for "chunk_241" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B	--- pretty_name: Evaluation run of Weyaxi/HelpSteer-filtered-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/HelpSteer-filtered-7B](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 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_Weyaxi__HelpSteer-filtered-7B\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T13:56:09.449355](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B/blob/main/results_2023-12-02T13-56-09.449355.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\": 0.33434420015163,\n\ \ \"acc_stderr\": 0.012994634003332771\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.33434420015163,\n \"acc_stderr\": 0.012994634003332771\n\ \ }\n}\n```" repo_url: https://huggingface.co/Weyaxi/HelpSteer-filtered-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_02T13_56_09.449355 path: - '/details_harness\|gsm8k\|5_2023-12-02T13-56-09.449355.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-02T13-56-09.449355.parquet' - config_name: results data_files: - split: 2023_12_02T13_56_09.449355 path: - results_2023-12-02T13-56-09.449355.parquet - split: latest path: - results_2023-12-02T13-56-09.449355.parquet --- # Dataset Card for Evaluation run of Weyaxi/HelpSteer-filtered-7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/HelpSteer-filtered-7B - 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 [Weyaxi/HelpSteer-filtered-7B](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 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_Weyaxi__HelpSteer-filtered-7B", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T13:56:09.449355](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B/blob/main/results_2023-12-02T13-56-09.449355.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": 0.33434420015163, "acc_stderr": 0.012994634003332771 }, "harness\|gsm8k\|5": { "acc": 0.33434420015163, "acc_stderr": 0.012994634003332771 } } ``` ### 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]
taaredikahan23/medical-llama2-1k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 394047 num_examples: 1000 download_size: 185327 dataset_size: 394047 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "medical-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/cv_svamp_augmented_fold3	--- dataset_info: features: - name: Question dtype: string - name: Numbers dtype: string - name: Equation dtype: string - name: Answer dtype: float64 - name: group_nums dtype: string - name: Body dtype: string - name: Ques dtype: string - name: question dtype: string - name: body dtype: string - name: equation dtype: string - name: wrong_equation dtype: string - name: WrongAnswer dtype: float64 - name: label dtype: float64 splits: - name: train num_bytes: 2824786 num_examples: 3973 - name: validation num_bytes: 129871 num_examples: 165 download_size: 953279 dataset_size: 2954657 --- # Dataset Card for "cv_svamp_augmented_fold3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hotchpotch/wikipedia-passages-jawiki-embeddings	--- license: other language: - ja --- wikipedia 日本語の文を、各種日本語の embeddings や faiss index へと変換したもの。 - [RAG用途に使える、Wikipedia 日本語の embeddings とベクトル検索用の faiss index を作った](https://secon.dev/entry/2023/12/04/080000-wikipedia-ja-embeddings/) - [HuggingFace Space 上のデモ](https://huggingface.co/spaces/hotchpotch/wikipedia-japanese-rag-qa) - [変換スクリプト](https://github.com/hotchpotch/wikipedia-passages-jawiki-embeddings-utils) ## 大元のデータ - https://huggingface.co/datasets/singletongue/wikipedia-utils ## 検索タスクでのデータ評価 - [ベクトル検索のみで、AI王クイズ第一回コンペに臨む - Q&Aタスクでの複数の日本語embeddingsの評価](https://secon.dev/entry/2023/12/21/080000-vector-search-ai-ou-comp/) - [OpenAIの新embeddings,text-embedding-3-smallをRAGタスクで評価する](https://secon.dev/entry/2024/01/29/100000-text-embedding-3-small/) ## ライセンス - `text-embedding-*` のファイルは OpenAI のライセンスに従います。 - それ以外は `CC-BY-SA-4.0` です
linyalan/python-bugs-name-noise-1	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: task dtype: string - name: prompt dtype: string - name: correct_code dtype: string - name: prompt_code dtype: string - name: full dtype: string - name: index dtype: int64 - name: noise_code dtype: string - name: noise_correct_code dtype: string splits: - name: train num_bytes: 3738893 num_examples: 1000 download_size: 1701993 dataset_size: 3738893 --- # Dataset Card for "python-bugs-name-noise-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vivienmeally/translated	--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 486930207 num_examples: 245000 download_size: 274569834 dataset_size: 486930207 --- # Dataset Card for "translated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
W4nkel/sounds	--- license: cc-by-4.0 ---
d071696/scraps1	--- task_categories: - image-classification - image-to-text - image-segmentation - image-feature-extraction language: - en size_categories: - n<1K ---
heyal/carbon_data	--- license: openrail ---
chiayewken/blocksworld	--- dataset_info: features: - name: domain dtype: string - name: instance dtype: string splits: - name: train num_bytes: 675434 num_examples: 501 download_size: 61032 dataset_size: 675434 configs: - config_name: default data_files: - split: train path: data/train-* --- # BlocksWorld This repo contains the BlocksWorld data for ["PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change"](https://arxiv.org/abs/2206.10498). The original data link is here: https://github.com/karthikv792/LLMs-Planning/tree/main/plan-bench/instances/blocksworld/generated
d0rj/rudetoxifier_data	--- dataset_info: features: - name: text dtype: string - name: toxic dtype: float64 splits: - name: train num_bytes: 27459998 num_examples: 163187 - name: test num_bytes: 1762288 num_examples: 10000 download_size: 16406619 dataset_size: 29222286 license: mit task_categories: - text-classification - text2text-generation language: - ru multilinguality: - monolingual tags: - toxicity - style-transfer pretty_name: RuDetoxifier data size_categories: - 100K<n<1M source_datasets: - original paperswithcode_id: methods-for-detoxification-of-texts-for-the --- # rudetoxifier_data ## Dataset Description - Homepage: https://github.com/s-nlp/rudetoxifier - Repository: https://github.com/s-nlp/rudetoxifier - Paper: [Methods for Detoxification of Texts for the Russian Language](https://arxiv.org/abs/2105.09052) - Point of Contact: [Daryna Dementieva](mailto:daryna.dementieva@skoltech.ru) Huggingface copy of Github repo with dataset.
EdwardLin2023/MELD-Audio	--- license: cc-by-4.0 ---
lucadiliello/relationextractionqa	--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers sequence: string - name: key dtype: string - name: labels list: - name: end sequence: int64 - name: start sequence: int64 splits: - name: test num_bytes: 863192 num_examples: 2948 download_size: 527428 dataset_size: 863192 --- # Dataset Card for "relationextractionqa" Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
datasets-examples/doc-image-10	--- size_categories: - n<1K --- # [doc] image dataset 10 This dataset contains a parquet file that contains an image column.
avermeersch/calabi-yau-threefolds	--- name: Reflexive Polyhedra of Calabi-Yau Threefolds date: '2023-09-28' license: other domain: Physics tags: - Calabi-Yau - Toric Geometry - String Theory - Polyhedra - Geometry - Physics pretty_name: Calabi-Yau 3-Folds size_categories: - 1K<n<10K --- # Dataset Card for Reflexive Polyhedra of Calabi-Yau Threefolds ## Table of Contents - [Dataset Description](#dataset-description) - [General Information](#general-information) - [Dataset Origin](#dataset-origin) - [Dataset Characteristics](#dataset-characteristics) - [Schema](#schema) - [Data Fields](#data-fields) - [Data Format](#data-format) - [Usage](#usage) - [Getting Started](#getting-started) - [Machine Learning Applications](#machine-learning-applications) - [Citations](#citations) ## Dataset Description ### General Information Calabi-Yau threefolds are a special class of smooth, compact three-dimensional spaces that have become fundamental objects in both mathematics and theoretical physics. In the context of string theory, they serve as the internal dimensions over which strings compactify, leading to a four-dimensional effective theory. The geometry of these threefolds is closely related to many physical phenomena, including the number of particle generations, gauge symmetries, and the cosmological constant. This dataset encompasses all 4319 reflexive polyhedra in 3 dimensions, offering a comprehensive view of potential Calabi-Yau geometries. The reflexive polyhedra serve as dual representations of these threefolds and are crucial in understanding their topological and geometric properties. ### Dataset Origin The dataset is derived from the original work documented in [hep-th/9805190](https://arxiv.org/abs/hep-th/9805190). While the original dataset was in a PALP-compatible structure, this version has been converted to a nested JSON format to better accommodate machine learning applications. The PALP-compatible version of the dataset can be accessed at [CYk3](http://hep.itp.tuwien.ac.at/~kreuzer/CY/CYk3.html). ## Dataset Characteristics ### Schema The dataset is presented in a nested JSON format, with each entry containing both metadata and a matrix representing the vertices of the corresponding polyhedron. ### Data Fields - `M1`, `M2`: These are point and vertex numbers in the M lattice, which is a mathematical lattice in the context of toric geometry. This lattice serves as the foundational geometric space from which the polyhedron is constructed. - `N1`, `N2`: Similar to the M lattice, these are point and vertex numbers in the N lattice, which is dual to the M lattice. The N lattice provides a different but equally important geometric perspective for understanding the polyhedron. - `Pic`: The Picard number is a topological invariant that measures the rank of the Néron-Severi group of a manifold. In the context of Calabi-Yau threefolds, it helps to determine the number of independent 2-cycles, which has physical implications like the number of U(1) gauge fields in the effective theory. - `Cor`: The correction term is a specific mathematical entity that adjusts the Picard number to account for certain topological peculiarities. The Picard numbers of a polyhedron and its dual add up to \( 20 + \text{correction} \). - `Matrix`: A 3xN matrix containing the coordinates of the vertices of the polyhedron. Each row represents a dimension in 3D space, and each column represents a vertex. ### Data Format Each entry in the dataset is structured as follows: ```json { "M1": ..., "M2": ..., "N1": ..., "N2": ..., "Pic": ..., "Cor": ..., "Matrix": [ [...], [...], [...] ] } ``` ## Usage ### Getting Started To access the dataset using the Hugging Face `datasets` library, the following Python code can be used: ```python from datasets import load_dataset dataset = load_dataset("calabi-yau-threefolds") ``` ### Machine Learning Applications This dataset provides rich opportunities for various machine learning tasks: - Geometric deep learning for topological invariant prediction. - Unsupervised learning techniques for polyhedra clustering. - Graph neural networks to model vertex connections. ### Citations For dataset usage, please cite the original paper using the following BibTeX entry: ```bibtex @misc{kreuzer1998classification, title={Classification of Reflexive Polyhedra in Three Dimensions}, author={M. Kreuzer and H. Skarke}, year={1998}, eprint={hep-th/9805190}, archivePrefix={arXiv}, primaryClass={hep-th} } ```
heliosprime/twitter_dataset_1713182160	--- 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: 19353 num_examples: 51 download_size: 18944 dataset_size: 19353 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713182160" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anan-2024/twitter_dataset_1712985523	--- 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: 20835 num_examples: 45 download_size: 11521 dataset_size: 20835 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/iyo_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of iyo/壱与/壹与 (Fate/Grand Order) This is the dataset of iyo/壱与/壹与 (Fate/Grand Order), containing 37 images and their tags. The core tags of this character are `long_hair, twintails, breasts, brown_hair, parted_bangs, large_breasts, very_long_hair, brown_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 \| 37 \| 52.48 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/iyo_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 37 \| 44.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/iyo_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 89 \| 84.15 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/iyo_fgo/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/iyo_fgo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](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, bare_shoulders, blush, grey_dress, sash, body_markings, sideboob, solo, white_dress, looking_at_viewer, smile, open_mouth, purple_eyes, small_breasts \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| blush \| grey_dress \| sash \| body_markings \| sideboob \| solo \| white_dress \| looking_at_viewer \| smile \| open_mouth \| purple_eyes \| small_breasts \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:--------\|:-------------\|:-------\|:----------------\|:-----------\|:-------\|:--------------\|:--------------------\|:--------\|:-------------\|:--------------\|:----------------\| \| 0 \| 10 \| ![](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 \|
Seongill/Trivia_missing_10_small	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 25984921 num_examples: 3771 download_size: 15465970 dataset_size: 25984921 configs: - config_name: default data_files: - split: train path: data/train-* ---
carloswylker/batista_forro	--- license: openrail ---
graphs-datasets/AIDS	--- licence: unknown task_categories: - graph-ml --- # Dataset Card for AIDS ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [External Use](#external-use) - [PyGeometric](#pygeometric) - [Dataset Structure](#dataset-structure) - [Data Properties](#data-properties) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [Homepage](https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data) - Paper:: (see citation) - Leaderboard:: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-aids) ### Dataset Summary The `AIDS` dataset is a dataset containing compounds checked for evidence of anti-HIV activity.. ### Supported Tasks and Leaderboards `AIDS` should be used for molecular classification, a binary classification task. The score used is accuracy with cross validation. ## External Use ### PyGeometric To load in PyGeometric, do the following: ```python from datasets import load_dataset from torch_geometric.data import Data from torch_geometric.loader import DataLoader dataset_hf = load_dataset("graphs-datasets/<mydataset>") # For the train set (replace by valid or test as needed) dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] dataset_pg = DataLoader(dataset_pg_list) ``` ## Dataset Structure ### Data Properties \| property \| value \| \|---\|---\| \| scale \| medium \| \| #graphs \| 1999 \| \| average #nodes \| 15.5875 \| \| average #edges \| 32.39 \| ### Data Fields Each row of a given file is a graph, with: - `node_feat` (list: #nodes x #node-features): nodes - `edge_index` (list: 2 x #edges): pairs of nodes constituting edges - `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features - `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one) - `num_nodes` (int): number of nodes of the graph ### Data Splits This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset. ## Additional Information ### Licensing Information The dataset has been released under license unknown. ### Citation Information ``` @inproceedings{Morris+2020, title={TUDataset: A collection of benchmark datasets for learning with graphs}, author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann}, booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)}, archivePrefix={arXiv}, eprint={2007.08663}, url={www.graphlearning.io}, year={2020} } ``` ``` @InProceedings{10.1007/978-3-540-89689-0_33, author="Riesen, Kaspar and Bunke, Horst", editor="da Vitoria Lobo, Niels and Kasparis, Takis and Roli, Fabio and Kwok, James T. and Georgiopoulos, Michael and Anagnostopoulos, Georgios C. and Loog, Marco", title="IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning", booktitle="Structural, Syntactic, and Statistical Pattern Recognition", year="2008", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="287--297", abstract="In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.", isbn="978-3-540-89689-0" } ```

liuyanchen1015/MULTI_VALUE_rte_a_ing

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 606461 num_examples: 1438 - name: train num_bytes: 540657 num_examples: 1284 download_size: 752975 dataset_size: 1147118 --- # Dataset Card for "MULTI_VALUE_rte_a_ing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nikutka/L1_poleval_korpus_wzorcowy_train

--- dataset_info: features: - name: content dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 20564 num_examples: 253 download_size: 15381 dataset_size: 20564 --- # Dataset Card for "L1_poleval_korpus_wzorcowy_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yangfu2/test

--- license: mit ---

Daniil-plotnikov/musicdataset

--- tags: - music --- This dataset is a text file with music in ABC format. A compositions is separated from another by two '###'

maximuslee07/raqna1.4k

--- license: llama2 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2507625 num_examples: 1581 download_size: 1451367 dataset_size: 2507625 configs: - config_name: default data_files: - split: train path: data/train-* ---

aagoluoglu/AI_HW3_frames

--- dataset_info: features: - name: video_id dtype: string - name: frame_num dtype: int64 - name: frame_encoded_base64 dtype: string - name: timestamp dtype: float64 splits: - name: train num_bytes: 563645985 num_examples: 344 download_size: 351336726 dataset_size: 563645985 configs: - config_name: default data_files: - split: train path: data/train-* ---

stoddur/medication_chat_4

--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 splits: - name: train num_bytes: 166152928.0 num_examples: 107612 download_size: 2515339 dataset_size: 166152928.0 --- # Dataset Card for "medication_chat_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

distilled-from-one-sec-cv12/chunk_247

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 813139740 num_examples: 158445 download_size: 827010115 dataset_size: 813139740 --- # Dataset Card for "chunk_247" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yaodi/libai

--- license: mit ---

gaizerick/gaizeric

--- license: openrail ---

tj-solergibert/SRV-T5-Europarl-mt-es

--- dataset_info: features: - name: source_text dtype: string - name: dest_text dtype: string - name: dest_lang dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 662085855 num_examples: 523542 - name: valid num_bytes: 91746842 num_examples: 71101 - name: test num_bytes: 95204696 num_examples: 73782 download_size: 275095415 dataset_size: 849037393 --- # Dataset Card for "SRV-T5-Europarl-mt-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2

--- pretty_name: Evaluation run of ZhangShenao/0.001_idpo_declr_4iters_iter_2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ZhangShenao/0.001_idpo_declr_4iters_iter_2](https://huggingface.co/ZhangShenao/0.001_idpo_declr_4iters_iter_2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-08T10:58:14.665887](https://huggingface.co/datasets/open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2/blob/main/results_2024-04-08T10-58-14.665887.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\": 0.6089111130070912,\n\ \ \"acc_stderr\": 0.033058238196911824,\n \"acc_norm\": 0.6153672426330791,\n\ \ \"acc_norm_stderr\": 0.033748541418571455,\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494884,\n \"mc2\": 0.48184218879604507,\n\ \ \"mc2_stderr\": 0.015696974795587824\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946709,\n\ \ \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.01415063143511173\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6548496315475005,\n\ \ \"acc_stderr\": 0.004744456628455121,\n \"acc_norm\": 0.8481378211511651,\n\ \ \"acc_norm_stderr\": 0.0035815378475817974\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\ \ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n\ \ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\ \ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4803921568627451,\n \"acc_stderr\": 0.04971358884367405,\n\ \ \"acc_norm\": 0.4803921568627451,\n \"acc_norm_stderr\": 0.04971358884367405\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4312169312169312,\n \"acc_stderr\": 0.025506481698138208,\n \"\ acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.025506481698138208\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n\ \ \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n\ \ \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7290322580645161,\n\ \ \"acc_stderr\": 0.025284416114900152,\n \"acc_norm\": 0.7290322580645161,\n\ \ \"acc_norm_stderr\": 0.025284416114900152\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\ : 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n\ \ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5538461538461539,\n \"acc_stderr\": 0.02520357177302833,\n \ \ \"acc_norm\": 0.5538461538461539,\n \"acc_norm_stderr\": 0.02520357177302833\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066475,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066475\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7853211009174312,\n \"acc_stderr\": 0.01760430414925648,\n \"\ acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.01760430414925648\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"\ acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159263,\n \ \ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159263\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n\ \ \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.043300437496507437,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.043300437496507437\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8058748403575989,\n\ \ \"acc_stderr\": 0.01414397027665757,\n \"acc_norm\": 0.8058748403575989,\n\ \ \"acc_norm_stderr\": 0.01414397027665757\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3229050279329609,\n\ \ \"acc_stderr\": 0.015638440380241488,\n \"acc_norm\": 0.3229050279329609,\n\ \ \"acc_norm_stderr\": 0.015638440380241488\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6633986928104575,\n \"acc_stderr\": 0.02705797462449438,\n\ \ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.02705797462449438\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6790123456790124,\n \"acc_stderr\": 0.025976566010862737,\n\ \ \"acc_norm\": 0.6790123456790124,\n \"acc_norm_stderr\": 0.025976566010862737\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4380704041720991,\n\ \ \"acc_stderr\": 0.012671902782567654,\n \"acc_norm\": 0.4380704041720991,\n\ \ \"acc_norm_stderr\": 0.012671902782567654\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.02916312857067073,\n\ \ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.02916312857067073\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6323529411764706,\n \"acc_stderr\": 0.019506291693954854,\n \ \ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.019506291693954854\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.046313813194254656,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.046313813194254656\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484378,\n\ \ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484378\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \ \ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322626\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494884,\n \"mc2\": 0.48184218879604507,\n\ \ \"mc2_stderr\": 0.015696974795587824\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7758484609313339,\n \"acc_stderr\": 0.011720400740774102\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.27824109173616374,\n \ \ \"acc_stderr\": 0.012343803671422673\n }\n}\n```" repo_url: https://huggingface.co/ZhangShenao/0.001_idpo_declr_4iters_iter_2 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_08T10_58_14.665887 path: - '**/details_harness|arc:challenge|25_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-08T10-58-14.665887.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|gsm8k|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hellaswag|10_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-08T10-58-14.665887.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-management|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T10-58-14.665887.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|truthfulqa:mc|0_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-08T10-58-14.665887.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_08T10_58_14.665887 path: - '**/details_harness|winogrande|5_2024-04-08T10-58-14.665887.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-08T10-58-14.665887.parquet' - config_name: results data_files: - split: 2024_04_08T10_58_14.665887 path: - results_2024-04-08T10-58-14.665887.parquet - split: latest path: - results_2024-04-08T10-58-14.665887.parquet --- # Dataset Card for Evaluation run of ZhangShenao/0.001_idpo_declr_4iters_iter_2  Dataset automatically created during the evaluation run of model [ZhangShenao/0.001_idpo_declr_4iters_iter_2](https://huggingface.co/ZhangShenao/0.001_idpo_declr_4iters_iter_2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-08T10:58:14.665887](https://huggingface.co/datasets/open-llm-leaderboard/details_ZhangShenao__0.001_idpo_declr_4iters_iter_2/blob/main/results_2024-04-08T10-58-14.665887.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": 0.6089111130070912, "acc_stderr": 0.033058238196911824, "acc_norm": 0.6153672426330791, "acc_norm_stderr": 0.033748541418571455, "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494884, "mc2": 0.48184218879604507, "mc2_stderr": 0.015696974795587824 }, "harness|arc:challenge|25": { "acc": 0.6006825938566553, "acc_stderr": 0.014312094557946709, "acc_norm": 0.6245733788395904, "acc_norm_stderr": 0.01415063143511173 }, "harness|hellaswag|10": { "acc": 0.6548496315475005, "acc_stderr": 0.004744456628455121, "acc_norm": 0.8481378211511651, "acc_norm_stderr": 0.0035815378475817974 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5838150289017341, "acc_stderr": 0.03758517775404947, "acc_norm": 0.5838150289017341, "acc_norm_stderr": 0.03758517775404947 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4803921568627451, "acc_stderr": 0.04971358884367405, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.04971358884367405 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4312169312169312, "acc_stderr": 0.025506481698138208, "acc_norm": 0.4312169312169312, "acc_norm_stderr": 0.025506481698138208 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.373015873015873, "acc_stderr": 0.04325506042017086, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.04325506042017086 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7290322580645161, "acc_stderr": 0.025284416114900152, "acc_norm": 0.7290322580645161, "acc_norm_stderr": 0.025284416114900152 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7626262626262627, "acc_stderr": 0.0303137105381989, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.0303137105381989 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548057, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548057 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5538461538461539, "acc_stderr": 0.02520357177302833, "acc_norm": 0.5538461538461539, "acc_norm_stderr": 0.02520357177302833 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.028317533496066475, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066475 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.634453781512605, "acc_stderr": 0.031282177063684614, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.031282177063684614 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526732, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526732 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7853211009174312, "acc_stderr": 0.01760430414925648, "acc_norm": 0.7853211009174312, "acc_norm_stderr": 0.01760430414925648 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4305555555555556, "acc_stderr": 0.03376922151252336, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.03376922151252336 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7637130801687764, "acc_stderr": 0.027652153144159263, "acc_norm": 0.7637130801687764, "acc_norm_stderr": 0.027652153144159263 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6681614349775785, "acc_stderr": 0.031602951437766785, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.031602951437766785 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6641221374045801, "acc_stderr": 0.041423137719966634, "acc_norm": 0.6641221374045801, "acc_norm_stderr": 0.041423137719966634 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7222222222222222, "acc_stderr": 0.043300437496507437, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.043300437496507437 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7361963190184049, "acc_stderr": 0.034624199316156234, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.034624199316156234 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165612, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165612 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8058748403575989, "acc_stderr": 0.01414397027665757, "acc_norm": 0.8058748403575989, "acc_norm_stderr": 0.01414397027665757 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3229050279329609, "acc_stderr": 0.015638440380241488, "acc_norm": 0.3229050279329609, "acc_norm_stderr": 0.015638440380241488 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6633986928104575, "acc_stderr": 0.02705797462449438, "acc_norm": 0.6633986928104575, "acc_norm_stderr": 0.02705797462449438 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6790123456790124, "acc_stderr": 0.025976566010862737, "acc_norm": 0.6790123456790124, "acc_norm_stderr": 0.025976566010862737 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4380704041720991, "acc_stderr": 0.012671902782567654, "acc_norm": 0.4380704041720991, "acc_norm_stderr": 0.012671902782567654 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6397058823529411, "acc_stderr": 0.02916312857067073, "acc_norm": 0.6397058823529411, "acc_norm_stderr": 0.02916312857067073 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6323529411764706, "acc_stderr": 0.019506291693954854, "acc_norm": 0.6323529411764706, "acc_norm_stderr": 0.019506291693954854 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6272727272727273, "acc_stderr": 0.046313813194254656, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.046313813194254656 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.689795918367347, "acc_stderr": 0.029613459872484378, "acc_norm": 0.689795918367347, "acc_norm_stderr": 0.029613459872484378 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368036, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368036 }, "harness|truthfulqa:mc|0": { "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494884, "mc2": 0.48184218879604507, "mc2_stderr": 0.015696974795587824 }, "harness|winogrande|5": { "acc": 0.7758484609313339, "acc_stderr": 0.011720400740774102 }, "harness|gsm8k|5": { "acc": 0.27824109173616374, "acc_stderr": 0.012343803671422673 } } ``` ## 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]

fathyshalab/reklamation24_schoenheit-wellness-full

--- dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction list: - name: label dtype: string - name: score dtype: float64 - name: prediction_agent dtype: string - name: annotation dtype: string - name: annotation_agent dtype: string - name: vectors struct: - name: mini-lm-sentence-transformers sequence: float64 - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 21984670 num_examples: 4158 download_size: 0 dataset_size: 21984670 --- # Dataset Card for "reklamation24_schoenheit-wellness-full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zolak/twitter_dataset_79_1713117433

--- 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: 264228 num_examples: 629 download_size: 140982 dataset_size: 264228 configs: - config_name: default data_files: - split: train path: data/train-* ---

jxie/wiki_books

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 22230107891 num_examples: 23464781 download_size: 13136529693 dataset_size: 22230107891 --- # Dataset Card for "wiki_books" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_mrpc_future_sub_gon

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 38745 num_examples: 143 - name: train num_bytes: 77103 num_examples: 284 - name: validation num_bytes: 11568 num_examples: 41 download_size: 95459 dataset_size: 127416 --- # Dataset Card for "MULTI_VALUE_mrpc_future_sub_gon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yangwang825/sst2-remove-stopwords-n2

--- 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: text dtype: string - name: label dtype: int64 - name: label_text dtype: string splits: - name: train num_bytes: 884164 num_examples: 6920 - name: validation num_bytes: 112712 num_examples: 872 - name: test num_bytes: 218641 num_examples: 1821 download_size: 722219 dataset_size: 1215517 --- # Dataset Card for "sst2-remove-stopwords-n2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sam1120/dropoff-utcustom-EVAL

--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 139241854.0 num_examples: 50 download_size: 40271598 dataset_size: 139241854.0 --- # Dataset Card for "dropoff-utcustom-EVAL" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Circularmachines/batch_indexing_machine_230529_012

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 152482128.0 num_examples: 720 download_size: 152492954 dataset_size: 152482128.0 --- # Dataset Card for "batch_indexing_machine_230529_012" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Sarjono/Tester

--- license: cc ---

RomeuForte/jacksparrow

--- license: openrail ---

irds/nyt_wksup_train

--- pretty_name: '`nyt/wksup/train`' viewer: false source_datasets: ['irds/nyt'] task_categories: - text-retrieval --- # Dataset Card for `nyt/wksup/train` The `nyt/wksup/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/nyt#nyt/wksup/train). # Data This dataset provides: - `queries` (i.e., topics); count=1,863,657 - `qrels`: (relevance assessments); count=1,863,657 - For `docs`, use [`irds/nyt`](https://huggingface.co/datasets/irds/nyt) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/nyt_wksup_train', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/nyt_wksup_train', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{MacAvaney2019Wksup, author = {MacAvaney, Sean and Yates, Andrew and Hui, Kai and Frieder, Ophir}, title = {Content-Based Weak Supervision for Ad-Hoc Re-Ranking}, booktitle = {SIGIR}, year = {2019} } @article{Sandhaus2008Nyt, title={The new york times annotated corpus}, author={Sandhaus, Evan}, journal={Linguistic Data Consortium, Philadelphia}, volume={6}, number={12}, pages={e26752}, year={2008} } ```

liuyanchen1015/MULTI_VALUE_mrpc_indef_one

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 199463 num_examples: 701 - name: train num_bytes: 441194 num_examples: 1560 - name: validation num_bytes: 47407 num_examples: 168 download_size: 457651 dataset_size: 688064 --- # Dataset Card for "MULTI_VALUE_mrpc_indef_one" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AdapterOcean/med_alpaca_standardized_cluster_34_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 13478218 num_examples: 7361 download_size: 6697396 dataset_size: 13478218 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_34_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kasvii/face-partuv2fulluv-ffhq10-samples

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 14738687.0 num_examples: 10 download_size: 14731902 dataset_size: 14738687.0 --- # Dataset Card for "face-partuv2fulluv-ffhq10-samples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

xfh/ontology_image_audio_2k

--- dataset_info: features: - name: text dtype: string - name: image dtype: image - name: audio dtype: audio - name: tag dtype: string - name: text_id dtype: string splits: - name: train num_examples: 2403 --- From https://github.com/audioset/ontology [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

anirhc/sutd_qa_dataset

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 94057.0 num_examples: 200 download_size: 46948 dataset_size: 94057.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/SpiderSQL

--- license: mit ---

kyriemao/topiocqa

--- license: apache-2.0 ---

suman895/sumanv

--- license: mit ---

CyberHarem/teeny_fireemblem

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of teeny (Fire Emblem) This is the dataset of teeny (Fire Emblem), containing 52 images and their tags. The core tags of this character are `long_hair, twintails, purple_eyes, purple_hair, multi-tied_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 | 52 | 43.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 52 | 28.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 86 | 49.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 52 | 39.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 86 | 66.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/teeny_fireemblem/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/teeny_fireemblem', 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 | 9 | ![](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, dress, looking_at_viewer, simple_background, solo, smile, white_background, closed_mouth, bare_shoulders, sleeveless, black_gloves, holding_book, jewelry | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | dress | looking_at_viewer | simple_background | solo | smile | white_background | closed_mouth | bare_shoulders | sleeveless | black_gloves | holding_book | jewelry | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:--------------------|:-------|:--------|:-------------------|:---------------|:-----------------|:-------------|:---------------|:---------------|:----------| | 0 | 9 | ![](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 |

C-MTEB/CmedqaRetrieval-qrels

--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: qid dtype: string - name: pid dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 595920 num_examples: 7449 download_size: 404005 dataset_size: 595920 --- # Dataset Card for "CmedqaRetrieval-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

howdi2000/may_v2

--- license: unknown ---

Jzuluaga/atcosim_corpus

--- dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: segment_start_time dtype: float32 - name: segment_end_time dtype: float32 - name: duration dtype: float32 splits: - name: test num_bytes: 471628915.76 num_examples: 1901 - name: train num_bytes: 1934757106.88 num_examples: 7638 download_size: 0 dataset_size: 2406386022.6400003 tags: - audio - automatic-speech-recognition - en-atc - en - robust-speech-recognition - noisy-speech-recognition - speech-recognition task_categories: - automatic-speech-recognition language: - en multilinguality: - monolingual --- # Dataset Card for ATCOSIM corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages and Other Details](#languages-and-other-details) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [ATCOSIM homepage](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) - **Repository:** [GitHub repository (used in research)](https://github.com/idiap/w2v2-air-traffic) - **Paper:** [The ATCOSIM Corpus of Non-Prompted Clean Air Traffic Control Speech](https://aclanthology.org/L08-1507/) - **Paper of this research:** [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822) ### Dataset Summary The ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database of air traffic control (ATC) operator speech, provided by Graz University of Technology (TUG) and Eurocontrol Experimental Centre (EEC). It consists of ten hours of speech data, which were recorded during ATC real-time simulations using a close-talk headset microphone. The utterances are in English language and pronounced by ten non-native speakers. The database includes orthographic transcriptions and additional information on speakers and recording sessions. It was recorded and annotated by Konrad Hofbauer ([description here](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html)). ### Supported Tasks and Leaderboards - `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [XLS-R-300m](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim). ### Languages and other details The text and the recordings are in English. The participating controllers were all actively employed air traffic controllers and possessed professional experience in the simulated sectors. The six male and four female controllers were of either German or Swiss nationality and had German, Swiss German or Swiss French native tongue. The controllers had agreed to the recording of their voice for the purpose of language analysis as well as for research and development in speech technologies, and were asked to show their normal working behaviour. ## Dataset Structure ### Data Fields - `id (string)`: a string of recording identifier for each example, corresponding to its. - `audio (audio)`: audio data for the given ID - `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc) - `segment_start_time (float32)`: segment start time (normally 0) - `segment_end_time (float32): segment end time - `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time ## Additional Information ### Licensing Information The licensing status of the dataset hinges on the legal status of the [ATCOSIM corpus](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) creators. ### Citation Information Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` Authors of the dataset: ``` @inproceedings{hofbauer-etal-2008-atcosim, title = "The {ATCOSIM} Corpus of Non-Prompted Clean Air Traffic Control Speech", author = "Hofbauer, Konrad and Petrik, Stefan and Hering, Horst", booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)", month = may, year = "2008", address = "Marrakech, Morocco", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/545_paper.pdf", } ```

irds/clueweb12_touche-2020-task-2

--- pretty_name: '`clueweb12/touche-2020-task-2`' viewer: false source_datasets: ['irds/clueweb12'] task_categories: - text-retrieval --- # Dataset Card for `clueweb12/touche-2020-task-2` The `clueweb12/touche-2020-task-2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/clueweb12#clueweb12/touche-2020-task-2). # Data This dataset provides: - `queries` (i.e., topics); count=50 - `qrels`: (relevance assessments); count=1,783 - For `docs`, use [`irds/clueweb12`](https://huggingface.co/datasets/irds/clueweb12) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/clueweb12_touche-2020-task-2', 'queries') for record in queries: record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...} qrels = load_dataset('irds/clueweb12_touche-2020-task-2', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Bondarenko2020Touche, address = {Berlin Heidelberg New York}, author = {Alexander Bondarenko and Maik Fr{\"o}be and Meriem Beloucif and Lukas Gienapp and Yamen Ajjour and Alexander Panchenko and Chris Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen}, booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 11th International Conference of the CLEF Association (CLEF 2020)}, doi = {10.1007/978-3-030-58219-7\_26}, editor = {Avi Arampatzis and Evangelos Kanoulas and Theodora Tsikrika and Stefanos Vrochidis and Hideo Joho and Christina Lioma and Carsten Eickhoff and Aur{\'e}lie N{\'e}v{\'e}ol and Linda Cappellato and Nicola Ferro}, month = sep, pages = {384-395}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, site = {Thessaloniki, Greece}, title = {{Overview of Touch{\'e} 2020: Argument Retrieval}}, url = {https://link.springer.com/chapter/10.1007/978-3-030-58219-7_26}, volume = 12260, year = 2020, } @inproceedings{Braunstain2016Support, author = {Liora Braunstain and Oren Kurland and David Carmel and Idan Szpektor and Anna Shtok}, editor = {Nicola Ferro and Fabio Crestani and Marie{-}Francine Moens and Josiane Mothe and Fabrizio Silvestri and Giorgio Maria Di Nunzio and Claudia Hauff and Gianmaria Silvello}, title = {Supporting Human Answers for Advice-Seeking Questions in {CQA} Sites}, booktitle = {Advances in Information Retrieval - 38th European Conference on {IR} Research, {ECIR} 2016, Padua, Italy, March 20-23, 2016. Proceedings}, series = {Lecture Notes in Computer Science}, volume = {9626}, pages = {129--141}, publisher = {Springer}, year = {2016}, doi = {10.1007/978-3-319-30671-1\_10}, } @inproceedings{Rafalak2014Credibility, author = {Maria Rafalak and Katarzyna Abramczuk and Adam Wierzbicki}, editor = {Chin{-}Wan Chung and Andrei Z. Broder and Kyuseok Shim and Torsten Suel}, title = {Incredible: is (almost) all web content trustworthy? analysis of psychological factors related to website credibility evaluation}, booktitle = {23rd International World Wide Web Conference, {WWW} '14, Seoul, Republic of Korea, April 7-11, 2014, Companion Volume}, pages = {1117--1122}, publisher = {{ACM}}, year = {2014}, doi = {10.1145/2567948.2578997}, } ```

CyberHarem/m1_garand_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of m1_garand/M1ガーランド/M1加兰德 (Girls' Frontline) This is the dataset of m1_garand/M1ガーランド/M1加兰德 (Girls' Frontline), containing 73 images and their tags. The core tags of this character are `long_hair, blonde_hair, green_eyes, breasts, large_breasts, hat, hairband, beret, bangs`, 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 | 73 | 98.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 73 | 51.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 174 | 112.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 73 | 85.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 174 | 168.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1_garand_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/m1_garand_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, blush, hair_flower, cleavage, looking_at_viewer, ponytail, solo, navel, sarong, green_bikini, hibiscus, rifle, smile, black_bikini, collarbone, hairclip, official_alternate_costume, open_mouth, sandals, sling | | 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) | 1boy, 1girl, blush, hetero, solo_focus, sweat, open_mouth, sex, censored, green_bikini, navel, penis, ponytail, arm_grab, cleavage, hair_between_eyes, hair_ornament, looking_at_viewer, male_pubic_hair, missionary, nude, on_back, on_bed, pussy, saliva, spread_legs, vaginal | | 2 | 41 | ![](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, solo, blue_skirt, looking_at_viewer, black_pantyhose, blush, brown_shirt, pleated_skirt, rifle, jacket, green_necktie, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | hair_flower | cleavage | looking_at_viewer | ponytail | solo | navel | sarong | green_bikini | hibiscus | rifle | smile | black_bikini | collarbone | hairclip | official_alternate_costume | open_mouth | sandals | sling | 1boy | hetero | solo_focus | sweat | sex | censored | penis | arm_grab | hair_between_eyes | hair_ornament | male_pubic_hair | missionary | nude | on_back | on_bed | pussy | saliva | spread_legs | vaginal | blue_skirt | black_pantyhose | brown_shirt | pleated_skirt | jacket | green_necktie | simple_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------|:-----------|:--------------------|:-----------|:-------|:--------|:---------|:---------------|:-----------|:--------|:--------|:---------------|:-------------|:-----------|:-----------------------------|:-------------|:----------|:--------|:-------|:---------|:-------------|:--------|:------|:-----------|:--------|:-----------|:--------------------|:----------------|:------------------|:-------------|:-------|:----------|:---------|:--------|:---------|:--------------|:----------|:-------------|:------------------|:--------------|:----------------|:---------|:----------------|:--------------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | 2 | 41 | ![](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 |

liuyanchen1015/MULTI_VALUE_qqp_if_would

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 597773 num_examples: 2940 - name: test num_bytes: 5891468 num_examples: 29705 - name: train num_bytes: 5380495 num_examples: 26549 download_size: 7271826 dataset_size: 11869736 --- # Dataset Card for "MULTI_VALUE_qqp_if_would" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

beltrewilton/punta-cana-spanish-reviews

--- license: mit task_categories: - text-classification language: - es --- This data set was collected for academic purposes, suitable for some NLP tasks including sentiment analysis.

jccoutojr/renatavoice

--- license: openrail ---

zolak/twitter_dataset_79_1713070286

--- 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: 2570928 num_examples: 6414 download_size: 1287018 dataset_size: 2570928 configs: - config_name: default data_files: - split: train path: data/train-* ---

allenai/ultrafeedback_binarized_cleaned

--- license: mit configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: source dtype: string splits: - name: train_sft num_bytes: 393926052.7984401 num_examples: 60829 - name: test_sft num_bytes: 6230841.363636363 num_examples: 985 - name: train_gen num_bytes: 314344767.49216783 num_examples: 60829 - name: test_gen num_bytes: 4982506.090909091 num_examples: 985 - name: train_prefs num_bytes: 393926052.7984401 num_examples: 60829 - name: test_prefs num_bytes: 12672623.615773508 num_examples: 1964 download_size: 629736515 dataset_size: 1126082844.1593668 --- # Dataset Card for "ultrafeedback_binarized_cleaned" **Update 1/12/2023**: I've removed examples identified as faulty by Argilla - see [their awesome work](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) for more details. This is a version of the [UltraFeedback binarized dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) but with TruthfulQA prompts removed and source annotations added (so you can filter out samples from different sources yourself if you want!). Please see the [binarized dataset card for more information](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), or the [original UltraFeedback dataset card](https://huggingface.co/datasets/openbmb/UltraFeedback).

andrinho1010/coringa.mp3

--- license: openrail ---

kye/all-pytorch-code

--- license: mit ---

benmccloskey/reviews

--- license: pddl ---

ymoslem/MediaSpeech

--- dataset_info: description: > MediaSpeech is a dataset of Arabic, French, Spanish, and Turkish media speech built with the purpose of testing Automated Speech Recognition (ASR) systems performance. features: - name: audio dtype: audio sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_examples: 10023 configs: - config_name: ar data_files: - split: train path: ar/train-* - config_name: fr data_files: - split: train path: fr/train-* - config_name: es data_files: - split: train path: es/train-* - config_name: tr data_files: - split: train path: tr/train-* license: cc-by-4.0 language: - ar - fr - es - tr pretty_name: MediaSpeech size_categories: - 1K<n<10K tags: - speech task_categories: - automatic-speech-recognition - text-to-speech --- # MediaSpeech MediaSpeech is a dataset of Arabic, French, Spanish, and Turkish media speech built with the purpose of testing Automated Speech Recognition (ASR) systems performance. The dataset contains 10 hours of speech for each language provided. The dataset consists of short speech segments automatically extracted from media videos available on YouTube and manually transcribed, with some pre-processing and post-processing. Baseline models and WAV version of the dataset can be found in this [git repository](https://github.com/NTRLab/MediaSpeech). ## How to load the dataset The dataset has 4 languages: Arabic (`ar`), Spanish (`es`), French (`fr`), and Turkish (`tr`). To load a language portion of the dataset: ``` from datasets import load_dataset downloaded_dataset = load_dataset("ymoslem/MediaSpeech", "ar", split="train") ``` ## Dataset structure The dataset structure is as follows: ``` DatasetDict({ train: Dataset({ features: ['audio', 'sentence'], num_rows: 2505 }) }) ``` ## Citation To cite the dataset, use the following BibTeX entry: ``` @misc{mediaspeech2021, title={MediaSpeech: Multilanguage ASR Benchmark and Dataset}, author={Rostislav Kolobov and Olga Okhapkina and Olga Omelchishina, Andrey Platunov and Roman Bedyakin and Vyacheslav Moshkin and Dmitry Menshikov and Nikolay Mikhaylovskiy}, year={2021}, eprint={2103.16193}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```

huggingartists/bones

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/bones" ## 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:** 1.235927 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/564dc935d7c601860b155b359d8ddf9d.1000x1000x1.png')"> </div> </div> <a href="https://huggingface.co/huggingartists/bones"> <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">BONES</div> <a href="https://genius.com/artists/bones"> <div style="text-align: center; font-size: 14px;">@bones</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/bones). ### 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/bones") ``` ## 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| |------:|---------:|---:| |1156| -| -| '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/bones") 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)

rouskinlab/lncRNA

--- license: mit language: - en tags: - chemistry - biology` author: Silvi Rouskin source: data.json date: 2024-03-19-12-30-41 --- # Data types - **sequence**: 10 datapoints - **structure**: 10 datapoints # Conversion report Over a total of 10 datapoints, there are: ### OUTPUT - ALL: 10 valid datapoints - INCLUDED: 0 duplicate sequences with different structure / dms / shape ### MODIFIED - 0 multiple sequences with the same reference (renamed reference) ### FILTERED OUT - 0 invalid datapoints (ex: sequence with non-regular characters) - 0 datapoints with bad structures - 0 duplicate sequences with the same structure / dms / shape

baylitoo/hello

--- dataset_info: features: - name: page_hash dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: normalized_bboxes sequence: sequence: int64 - name: labels sequence: int64 - name: original_image dtype: image splits: - name: train num_bytes: 4038206084.848 num_examples: 15009 - name: validation num_bytes: 407608434.304 num_examples: 1607 - name: test num_bytes: 268092681.752 num_examples: 1041 download_size: 1783996007 dataset_size: 4713907200.904 --- # Dataset Card for "hello" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-elementary_mathematics-original-neg-prepend

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_prompt dtype: string splits: - name: test num_bytes: 5179 num_examples: 14 download_size: 7035 dataset_size: 5179 --- # Dataset Card for "mmlu-elementary_mathematics-original-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

prantadi/tokenized_dataset_intent_3600

--- license: apache-2.0 ---

open-llm-leaderboard/details_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup

--- pretty_name: Evaluation run of jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup](https://huggingface.co/jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup)\ \ 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_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-12T08:24:49.664707](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup/blob/main/results_2024-02-12T08-24-49.664707.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\": 0.604711622779494,\n\ \ \"acc_stderr\": 0.032467251718581315,\n \"acc_norm\": 0.6163715674554368,\n\ \ \"acc_norm_stderr\": 0.03334110735529664,\n \"mc1\": 0.2937576499388005,\n\ \ \"mc1_stderr\": 0.015945068581236618,\n \"mc2\": 0.45375623938134657,\n\ \ \"mc2_stderr\": 0.015248519436290428\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520763,\n\ \ \"acc_norm\": 0.6006825938566553,\n \"acc_norm_stderr\": 0.014312094557946704\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6222863971320454,\n\ \ \"acc_stderr\": 0.004838246410786262,\n \"acc_norm\": 0.8218482374029078,\n\ \ \"acc_norm_stderr\": 0.0038185843846355303\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n\ \ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n\ \ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\ \ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.03745554791462456,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.03745554791462456\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n\ \ \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n\ \ \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.35978835978835977,\n \"acc_stderr\": 0.02471807594412928,\n \"\ acc_norm\": 0.35978835978835977,\n \"acc_norm_stderr\": 0.02471807594412928\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7064516129032258,\n\ \ \"acc_stderr\": 0.025906087021319295,\n \"acc_norm\": 0.7064516129032258,\n\ \ \"acc_norm_stderr\": 0.025906087021319295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.034953345821629345,\n\ \ \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.034953345821629345\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.0291265228345868,\n \"acc_norm\"\ : 0.7878787878787878,\n \"acc_norm_stderr\": 0.0291265228345868\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.02649905770139744,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.02649905770139744\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6076923076923076,\n \"acc_stderr\": 0.02475600038213095,\n \ \ \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.02475600038213095\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.27037037037037037,\n \"acc_stderr\": 0.02708037281514565,\n \ \ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.02708037281514565\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\ \ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7743119266055046,\n \"acc_stderr\": 0.017923087667803064,\n \"\ acc_norm\": 0.7743119266055046,\n \"acc_norm_stderr\": 0.017923087667803064\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5740740740740741,\n \"acc_stderr\": 0.033723432716530624,\n \"\ acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.033723432716530624\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931048,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931048\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8227848101265823,\n \"acc_stderr\": 0.02485636418450322,\n \ \ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.02485636418450322\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\ \ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\ : 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n\ \ \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7982120051085568,\n\ \ \"acc_stderr\": 0.014351702181636863,\n \"acc_norm\": 0.7982120051085568,\n\ \ \"acc_norm_stderr\": 0.014351702181636863\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.28938547486033517,\n\ \ \"acc_stderr\": 0.015166544550490298,\n \"acc_norm\": 0.28938547486033517,\n\ \ \"acc_norm_stderr\": 0.015166544550490298\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.026716118380156847,\n\ \ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.026716118380156847\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811032,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811032\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6882716049382716,\n \"acc_stderr\": 0.025773111169630457,\n\ \ \"acc_norm\": 0.6882716049382716,\n \"acc_norm_stderr\": 0.025773111169630457\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4667535853976532,\n\ \ \"acc_stderr\": 0.012741974333897224,\n \"acc_norm\": 0.4667535853976532,\n\ \ \"acc_norm_stderr\": 0.012741974333897224\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6258169934640523,\n \"acc_stderr\": 0.019576953122088837,\n \ \ \"acc_norm\": 0.6258169934640523,\n \"acc_norm_stderr\": 0.019576953122088837\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910508,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910508\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.029393609319879804,\n\ \ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.029393609319879804\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n\ \ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n\ \ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.03218093795602357,\n\ \ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.03218093795602357\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2937576499388005,\n\ \ \"mc1_stderr\": 0.015945068581236618,\n \"mc2\": 0.45375623938134657,\n\ \ \"mc2_stderr\": 0.015248519436290428\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233626\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup 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_02_12T08_24_49.664707 path: - '**/details_harness|arc:challenge|25_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-12T08-24-49.664707.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|gsm8k|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hellaswag|10_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-12T08-24-49.664707.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-management|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-12T08-24-49.664707.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|truthfulqa:mc|0_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-12T08-24-49.664707.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_12T08_24_49.664707 path: - '**/details_harness|winogrande|5_2024-02-12T08-24-49.664707.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-12T08-24-49.664707.parquet' - config_name: results data_files: - split: 2024_02_12T08_24_49.664707 path: - results_2024-02-12T08-24-49.664707.parquet - split: latest path: - results_2024-02-12T08-24-49.664707.parquet --- # Dataset Card for Evaluation run of jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup  Dataset automatically created during the evaluation run of model [jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup](https://huggingface.co/jingyeom/KoSoLAR-10.7B-v0.2_1.4_dedup) 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_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-12T08:24:49.664707](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__KoSoLAR-10.7B-v0.2_1.4_dedup/blob/main/results_2024-02-12T08-24-49.664707.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": 0.604711622779494, "acc_stderr": 0.032467251718581315, "acc_norm": 0.6163715674554368, "acc_norm_stderr": 0.03334110735529664, "mc1": 0.2937576499388005, "mc1_stderr": 0.015945068581236618, "mc2": 0.45375623938134657, "mc2_stderr": 0.015248519436290428 }, "harness|arc:challenge|25": { "acc": 0.5750853242320819, "acc_stderr": 0.014445698968520763, "acc_norm": 0.6006825938566553, "acc_norm_stderr": 0.014312094557946704 }, "harness|hellaswag|10": { "acc": 0.6222863971320454, "acc_stderr": 0.004838246410786262, "acc_norm": 0.8218482374029078, "acc_norm_stderr": 0.0038185843846355303 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04292596718256981, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04292596718256981 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001974, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7222222222222222, "acc_stderr": 0.03745554791462456, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.03745554791462456 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, 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"harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4433497536945813, "acc_stderr": 0.034953345821629345, "acc_norm": 0.4433497536945813, "acc_norm_stderr": 0.034953345821629345 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8, "acc_stderr": 0.031234752377721175, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721175 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.0291265228345868, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.0291265228345868 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8393782383419689, "acc_stderr": 0.02649905770139744, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.02649905770139744 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6076923076923076, "acc_stderr": 0.02475600038213095, "acc_norm": 0.6076923076923076, "acc_norm_stderr": 0.02475600038213095 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.27037037037037037, "acc_stderr": 0.02708037281514565, "acc_norm": 0.27037037037037037, "acc_norm_stderr": 0.02708037281514565 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6428571428571429, "acc_stderr": 0.031124619309328177, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.031124619309328177 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526732, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526732 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7743119266055046, "acc_stderr": 0.017923087667803064, "acc_norm": 0.7743119266055046, "acc_norm_stderr": 0.017923087667803064 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5740740740740741, "acc_stderr": 0.033723432716530624, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.033723432716530624 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931048, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931048 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8227848101265823, "acc_stderr": 0.02485636418450322, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.02485636418450322 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness|hendrycksTest-international_law|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302872, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302872 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7116564417177914, "acc_stderr": 0.035590395316173425, "acc_norm": 0.7116564417177914, "acc_norm_stderr": 0.035590395316173425 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.41964285714285715, "acc_stderr": 0.046840993210771065, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.046840993210771065 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281376, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281376 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, 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0.6882716049382716, "acc_stderr": 0.025773111169630457, "acc_norm": 0.6882716049382716, "acc_norm_stderr": 0.025773111169630457 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4667535853976532, "acc_stderr": 0.012741974333897224, "acc_norm": 0.4667535853976532, "acc_norm_stderr": 0.012741974333897224 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.0286619962023353, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.0286619962023353 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6258169934640523, "acc_stderr": 0.019576953122088837, "acc_norm": 0.6258169934640523, "acc_norm_stderr": 0.019576953122088837 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910508, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910508 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6979591836734694, "acc_stderr": 0.029393609319879804, "acc_norm": 0.6979591836734694, "acc_norm_stderr": 0.029393609319879804 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7860696517412935, "acc_stderr": 0.02899690969332891, "acc_norm": 0.7860696517412935, "acc_norm_stderr": 0.02899690969332891 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness|hendrycksTest-virology|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7719298245614035, "acc_stderr": 0.03218093795602357, "acc_norm": 0.7719298245614035, "acc_norm_stderr": 0.03218093795602357 }, "harness|truthfulqa:mc|0": { "mc1": 0.2937576499388005, "mc1_stderr": 0.015945068581236618, "mc2": 0.45375623938134657, "mc2_stderr": 0.015248519436290428 }, "harness|winogrande|5": { "acc": 0.7466456195737964, "acc_stderr": 0.012223754434233626 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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 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LinhDuong/mito

--- license: apache-2.0 size_categories: - n<1K tags: - biology dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 259624242.0 num_examples: 165 download_size: 136645365 dataset_size: 259624242.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/tanaka_asuka_soundeuphonium

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tanaka Asuka/田中あすか (Sound! Euphonium) This is the dataset of Tanaka Asuka/田中あすか (Sound! Euphonium), containing 479 images and their tags. The core tags of this character are `black_hair, long_hair, glasses, semi-rimless_eyewear, blue_eyes, red-framed_eyewear, over-rim_eyewear, hair_between_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 | 479 | 321.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_asuka_soundeuphonium/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 479 | 320.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_asuka_soundeuphonium/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 908 | 567.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_asuka_soundeuphonium/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/tanaka_asuka_soundeuphonium', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](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) | blush, green_neckerchief, kitauji_high_school_uniform, looking_at_viewer, sailor_collar, serafuku, solo_focus, 2girls, skirt, smile, 1girl, blurry | | 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) | 1girl, green_neckerchief, kitauji_high_school_uniform, looking_at_viewer, sailor_collar, serafuku, solo, instrument | | 2 | 26 | ![](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, blush, kitauji_high_school_uniform, sailor_collar, serafuku, green_neckerchief, looking_at_viewer, solo, smile | | 3 | 9 | ![](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, blue_sailor_collar, blue_skirt, blush, green_neckerchief, kitauji_high_school_uniform, pleated_skirt, serafuku, short_sleeves, solo, white_shirt, indoors, open_mouth, smile, looking_at_viewer | | 4 | 6 | ![](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, green_neckerchief, kitauji_high_school_uniform, serafuku, short_sleeves, solo, white_shirt, blue_sailor_collar, upper_body, closed_mouth | | 5 | 17 | ![](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, black_pantyhose, kitauji_high_school_uniform, pleated_skirt, serafuku, solo, green_neckerchief, smile, long_sleeves, white_sailor_collar, brown_skirt, indoors, looking_at_viewer, open_mouth, chalkboard | | 6 | 9 | ![](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) | chair, classroom, green_neckerchief, indoors, instrument, kitauji_high_school_uniform, serafuku, sitting, 1girl, solo, chalkboard, blue_skirt, black_pantyhose, blue_sailor_collar, desk, pleated_skirt, short_sleeves, holding | | 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, looking_at_viewer, scarf, snowing, solo, upper_body, coat, smile, winter_clothes, blush, hands_on_own_face, outdoors | | 8 | 5 | ![](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, anime_coloring, lipstick, necklace, solo, blurry, collarbone, ponytail, shirt, indoors, hair_ornament, looking_at_viewer, sweatdrop | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | green_neckerchief | kitauji_high_school_uniform | looking_at_viewer | sailor_collar | serafuku | solo_focus | 2girls | skirt | smile | 1girl | blurry | solo | instrument | blue_sailor_collar | blue_skirt | pleated_skirt | short_sleeves | white_shirt | indoors | open_mouth | upper_body | closed_mouth | black_pantyhose | long_sleeves | white_sailor_collar | brown_skirt | chalkboard | chair | classroom | sitting | desk | holding | scarf | snowing | coat | winter_clothes | hands_on_own_face | outdoors | anime_coloring | lipstick | necklace | collarbone | ponytail | shirt | hair_ornament | sweatdrop | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:------------------------------|:--------------------|:----------------|:-----------|:-------------|:---------|:--------|:--------|:--------|:---------|:-------|:-------------|:---------------------|:-------------|:----------------|:----------------|:--------------|:----------|:-------------|:-------------|:---------------|:------------------|:---------------|:----------------------|:--------------|:-------------|:--------|:------------|:----------|:-------|:----------|:--------|:----------|:-------|:-----------------|:--------------------|:-----------|:-----------------|:-----------|:-----------|:-------------|:-----------|:--------|:----------------|:------------| | 0 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 26 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 17 | ![](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 | | | | | | | | | | | | | | | | | | | | | 6 | 9 | ![](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 | | | | | | | | | | | | | | | | 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 | | | | | | | | | | 8 | 5 | ![](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 |

psyche/nmt-sample

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: source_language dtype: string - name: target_language dtype: string - name: category dtype: string splits: - name: train num_bytes: 988 num_examples: 3 download_size: 5473 dataset_size: 988 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "nmt-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_mrpc_perfect_slam

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 42425 num_examples: 144 - name: train num_bytes: 103515 num_examples: 367 - name: validation num_bytes: 12295 num_examples: 44 download_size: 113577 dataset_size: 158235 --- # Dataset Card for "MULTI_VALUE_mrpc_perfect_slam" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

selenalu/data

--- configs: - config_name: default data_files: - split: train path: '**/*.jsonl' --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

aisc-team-d2/healthsearchqa

--- dataset_info: features: - name: id dtype: float64 - name: question dtype: string splits: - name: train num_bytes: 170966 num_examples: 4436 download_size: 79303 dataset_size: 170966 configs: - config_name: default data_files: - split: train path: data/train-* license: unknown task_categories: - question-answering language: - en tags: - medical size_categories: - 1K<n<10K --- # HealthSearchQA Dataset of consumer health questions released by Google for the Med-PaLM paper ([arXiv preprint](https://arxiv.org/abs/2212.13138)). From the [paper](https://www.nature.com/articles/s41586-023-06291-2): We curated our own additional dataset consisting of 3,173 commonly searched consumer questions, referred to as HealthSearchQA. The dataset was curated using seed medical conditions and their associated symptoms. We used the seed data to retrieve publicly-available commonly searched questions generated by a search engine, which were displayed to all users entering the seed terms. We publish the dataset as an open benchmark for answering medical questions from consumers and hope this will be a useful resource for the community, as a dataset reflecting real-world consumer concerns. **Format:** Question only, free text response, open domain. **Size:** 3,173. **Example question:** How serious is atrial fibrillation? **Example question:** What kind of cough comes with Covid? **Example question:** Is blood in phlegm serious?

HOXSEC/cs2-maps

--- license: mit ---

clinicalnlplab/medMCQA_test

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: train num_bytes: 2873180 num_examples: 4183 - name: valid num_bytes: 2873180 num_examples: 4183 - name: test num_bytes: 2873180 num_examples: 4183 download_size: 2161257 dataset_size: 8619540 --- # Dataset Card for "medMCQA_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

soban09/myntra-men-formal-shirt

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 85061.0 num_examples: 10 download_size: 82189 dataset_size: 85061.0 --- # Dataset Card for "myntra-men-formal-shirt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

communityai/apt-orion-math-test-v1

--- dataset_info: features: - name: tokens dtype: int64 - name: uid dtype: string - name: source dtype: string - name: system dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 42760980.0 num_examples: 1694 download_size: 18070881 dataset_size: 42760980.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/tapris_sugarbell_chisaki_gabrieldropout

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tapris Sugarbell Chisaki This is the dataset of Tapris Sugarbell Chisaki, containing 75 images and their tags. 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)). | Name | Images | Download | Description | |:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------| | raw | 75 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 163 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 224 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 75 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 75 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 75 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 163 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 163 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 148 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 224 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 224 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

jovianzm/Pexels-400k

--- license: mit task_categories: - image-to-text - text-to-image - text-to-video - image-to-video language: - en pretty_name: Pexels-400k size_categories: - 100K<n<1M --- # Pexels 400k Dataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption. Note: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed.

CyberHarem/m1918_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of m1918/M1918/M1918 (Girls' Frontline) This is the dataset of m1918/M1918/M1918 (Girls' Frontline), containing 79 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, sunglasses, ahoge, eyewear_on_head, large_breasts, aviator_sunglasses, green_eyes, very_long_hair, bangs, low-tied_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 | 79 | 98.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 79 | 54.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 180 | 114.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 79 | 85.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1918_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 180 | 160.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1918_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/m1918_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 | 22 | ![](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, blush, detached_collar, looking_at_viewer, official_alternate_costume, playboy_bunny, bare_shoulders, open_mouth, rabbit_ears, solo, fake_animal_ears, black_leotard, necktie, cleavage, fishnets, wrist_cuffs, white_background, high_heels, holding, machine_gun, black_footwear, full_body, hair_flaps, standing, stuffed_animal, black_pantyhose, simple_background, strapless_leotard | | 1 | 8 | ![](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, open_mouth, solo, black_dress, blush, cleavage, official_alternate_costume, machine_gun, thighhighs, necklace, black_gloves, full_body, high_heels, looking_at_viewer, bare_shoulders, black_choker, black_footwear, collarbone, covering, formal, torn_clothes | | 2 | 14 | ![](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, green_necktie, solo, holding_gun, open_mouth, black_skirt, blush, machine_gun, shirt, white_thighhighs, looking_at_viewer, military_uniform, pleated_skirt, simple_background, black_jacket, miniskirt, shoes, white_background, black_footwear, long_sleeves, green_bow, hair_bow, hair_flaps, standing, ass, hair_between_eyes, smile | | 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, solo, green_necktie, hair_flaps, pleated_skirt, shirt, smile, black_skirt, blush, hair_between_eyes, looking_at_viewer, open_mouth, blazer, military_uniform, simple_background, white_background, yellow_eyes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | detached_collar | looking_at_viewer | official_alternate_costume | playboy_bunny | bare_shoulders | open_mouth | rabbit_ears | solo | fake_animal_ears | black_leotard | necktie | cleavage | fishnets | wrist_cuffs | white_background | high_heels | holding | machine_gun | black_footwear | full_body | hair_flaps | standing | stuffed_animal | black_pantyhose | simple_background | strapless_leotard | black_dress | thighhighs | necklace | black_gloves | black_choker | collarbone | covering | formal | torn_clothes | green_necktie | holding_gun | black_skirt | shirt | white_thighhighs | military_uniform | pleated_skirt | black_jacket | miniskirt | shoes | long_sleeves | green_bow | hair_bow | ass | hair_between_eyes | smile | blazer | yellow_eyes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:------------------|:--------------------|:-----------------------------|:----------------|:-----------------|:-------------|:--------------|:-------|:-------------------|:----------------|:----------|:-----------|:-----------|:--------------|:-------------------|:-------------|:----------|:--------------|:-----------------|:------------|:-------------|:-----------|:-----------------|:------------------|:--------------------|:--------------------|:--------------|:-------------|:-----------|:---------------|:---------------|:-------------|:-----------|:---------|:---------------|:----------------|:--------------|:--------------|:--------|:-------------------|:-------------------|:----------------|:---------------|:------------|:--------|:---------------|:------------|:-----------|:------|:--------------------|:--------|:---------|:--------------| | 0 | 22 | ![](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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 | ![](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 | 14 | ![](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 | 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 |

heliosprime/twitter_dataset_1713052381

--- 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: 15743 num_examples: 35 download_size: 10294 dataset_size: 15743 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713052381" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Codec-SUPERB/beijing_opera_unit

--- configs: - config_name: default data_files: - 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: id dtype: string - name: unit sequence: sequence: int64 splits: - name: academicodec_hifi_16k_320d num_bytes: 1808834 num_examples: 236 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 1808834 num_examples: 236 - name: academicodec_hifi_24k_320d num_bytes: 2707522 num_examples: 236 - name: audiodec_24k_320d num_bytes: 5784962 num_examples: 236 - name: dac_16k num_bytes: 5433794 num_examples: 236 - name: dac_24k num_bytes: 21666818 num_examples: 236 - name: dac_44k num_bytes: 6999890 num_examples: 236 - name: encodec_24k_12bps num_bytes: 10837250 num_examples: 236 - name: encodec_24k_1_5bps num_bytes: 1361378 num_examples: 236 - name: encodec_24k_24bps num_bytes: 21666818 num_examples: 236 - name: encodec_24k_3bps num_bytes: 2715074 num_examples: 236 - name: encodec_24k_6bps num_bytes: 5422466 num_examples: 236 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 7287810 num_examples: 236 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 14477314 num_examples: 236 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 7287810 num_examples: 236 - name: speech_tokenizer_16k num_bytes: 3625090 num_examples: 236 download_size: 16959778 dataset_size: 164323606 --- # Dataset Card for "beijing_opera_unit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/50e86b1c

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1336 dataset_size: 182 --- # Dataset Card for "50e86b1c" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

smithr14buckeye/whistlingflagsdataset

--- language: - en pretty_name: Hole and Round Stats ---

anan-2024/twitter_dataset_1712959978

--- 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: 22523 num_examples: 49 download_size: 13744 dataset_size: 22523 configs: - config_name: default data_files: - split: train path: data/train-* ---

tabcoin/test

--- license: openrail ---

d4rk3r/food

--- license: mit ---

saibo/wikinre_catalog

--- dataset_info: features: - name: en_label dtype: string splits: - name: entity num_bytes: 5123074 num_examples: 278843 - name: relation num_bytes: 2887 num_examples: 158 download_size: 4526528 dataset_size: 5125961 language: - en size_categories: - 100K<n<1M --- # Dataset Card for "wikinre_catalog" Catalog for https://huggingface.co/datasets/saibo/wiki-nre This catalog can be used to do constrained generation. All entities and relations can be found in wikidata by searching https://www.wikidata.org/wiki/ [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

friedrichor/PhotoChat

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4582797667.34 num_examples: 8540 download_size: 4857362900 dataset_size: 4582797667.34 --- # Dataset Card for "PhotoChat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Pablao0948/Datena

--- license: openrail ---

mateuzim/LAUANA86

--- license: openrail ---

quocanh34/test_result_large_synthesis_data_tunelm

--- dataset_info: features: - name: id dtype: string - name: pred_str dtype: string - name: test_norm dtype: string splits: - name: train num_bytes: 208389 num_examples: 1299 download_size: 109281 dataset_size: 208389 --- # Dataset Card for "test_result_large_synthesis_data_tunelm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/kawashima_mizuki_idolmastercinderellagirls

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kawashima_mizuki/川島瑞樹 (THE iDOLM@STER: Cinderella Girls) This is the dataset of kawashima_mizuki/川島瑞樹 (THE iDOLM@STER: Cinderella Girls), containing 169 images and their tags. The core tags of this character are `brown_hair, brown_eyes, long_hair, breasts, ponytail`, 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 | 169 | 133.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 169 | 102.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 315 | 182.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 169 | 127.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 315 | 223.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashima_mizuki_idolmastercinderellagirls/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/kawashima_mizuki_idolmastercinderellagirls', 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 | 8 | ![](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, open_mouth, solo, blush, necklace, smile, large_breasts, looking_at_viewer, bracelet, cleavage | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | open_mouth | solo | blush | necklace | smile | large_breasts | looking_at_viewer | bracelet | cleavage | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:-------|:--------|:-----------|:--------|:----------------|:--------------------|:-----------|:-----------| | 0 | 8 | ![](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 |

crewdon/instructionPairedFormularDataset13k

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 3190559 num_examples: 13655 download_size: 1482698 dataset_size: 3190559 --- # Dataset Card for "instructionPairedFormularDataset13k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-one-sec-cv12/chunk_241

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1453450296 num_examples: 285438 download_size: 1484410260 dataset_size: 1453450296 --- # Dataset Card for "chunk_241" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B

--- pretty_name: Evaluation run of Weyaxi/HelpSteer-filtered-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/HelpSteer-filtered-7B](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 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_Weyaxi__HelpSteer-filtered-7B\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T13:56:09.449355](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B/blob/main/results_2023-12-02T13-56-09.449355.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\": 0.33434420015163,\n\ \ \"acc_stderr\": 0.012994634003332771\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.33434420015163,\n \"acc_stderr\": 0.012994634003332771\n\ \ }\n}\n```" repo_url: https://huggingface.co/Weyaxi/HelpSteer-filtered-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_02T13_56_09.449355 path: - '**/details_harness|gsm8k|5_2023-12-02T13-56-09.449355.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-02T13-56-09.449355.parquet' - config_name: results data_files: - split: 2023_12_02T13_56_09.449355 path: - results_2023-12-02T13-56-09.449355.parquet - split: latest path: - results_2023-12-02T13-56-09.449355.parquet --- # Dataset Card for Evaluation run of Weyaxi/HelpSteer-filtered-7B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Weyaxi/HelpSteer-filtered-7B - **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 [Weyaxi/HelpSteer-filtered-7B](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 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_Weyaxi__HelpSteer-filtered-7B", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T13:56:09.449355](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__HelpSteer-filtered-7B/blob/main/results_2023-12-02T13-56-09.449355.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": 0.33434420015163, "acc_stderr": 0.012994634003332771 }, "harness|gsm8k|5": { "acc": 0.33434420015163, "acc_stderr": 0.012994634003332771 } } ``` ### 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]

taaredikahan23/medical-llama2-1k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 394047 num_examples: 1000 download_size: 185327 dataset_size: 394047 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "medical-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sethapun/cv_svamp_augmented_fold3

--- dataset_info: features: - name: Question dtype: string - name: Numbers dtype: string - name: Equation dtype: string - name: Answer dtype: float64 - name: group_nums dtype: string - name: Body dtype: string - name: Ques dtype: string - name: question dtype: string - name: body dtype: string - name: equation dtype: string - name: wrong_equation dtype: string - name: WrongAnswer dtype: float64 - name: label dtype: float64 splits: - name: train num_bytes: 2824786 num_examples: 3973 - name: validation num_bytes: 129871 num_examples: 165 download_size: 953279 dataset_size: 2954657 --- # Dataset Card for "cv_svamp_augmented_fold3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hotchpotch/wikipedia-passages-jawiki-embeddings

--- license: other language: - ja --- wikipedia 日本語の文を、各種日本語の embeddings や faiss index へと変換したもの。 - [RAG用途に使える、Wikipedia 日本語の embeddings とベクトル検索用の faiss index を作った](https://secon.dev/entry/2023/12/04/080000-wikipedia-ja-embeddings/) - [HuggingFace Space 上のデモ](https://huggingface.co/spaces/hotchpotch/wikipedia-japanese-rag-qa) - [変換スクリプト](https://github.com/hotchpotch/wikipedia-passages-jawiki-embeddings-utils) ## 大元のデータ - https://huggingface.co/datasets/singletongue/wikipedia-utils ## 検索タスクでのデータ評価 - [ベクトル検索のみで、AI王クイズ第一回コンペに臨む - Q&Aタスクでの複数の日本語embeddingsの評価](https://secon.dev/entry/2023/12/21/080000-vector-search-ai-ou-comp/) - [OpenAIの新embeddings,text-embedding-3-smallをRAGタスクで評価する](https://secon.dev/entry/2024/01/29/100000-text-embedding-3-small/) ## ライセンス - `text-embedding-*` のファイルは OpenAI のライセンスに従います。 - それ以外は `CC-BY-SA-4.0` です

linyalan/python-bugs-name-noise-1

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: task dtype: string - name: prompt dtype: string - name: correct_code dtype: string - name: prompt_code dtype: string - name: full dtype: string - name: index dtype: int64 - name: noise_code dtype: string - name: noise_correct_code dtype: string splits: - name: train num_bytes: 3738893 num_examples: 1000 download_size: 1701993 dataset_size: 3738893 --- # Dataset Card for "python-bugs-name-noise-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vivienmeally/translated

--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 486930207 num_examples: 245000 download_size: 274569834 dataset_size: 486930207 --- # Dataset Card for "translated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

W4nkel/sounds

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

d071696/scraps1

--- task_categories: - image-classification - image-to-text - image-segmentation - image-feature-extraction language: - en size_categories: - n<1K ---

heyal/carbon_data

--- license: openrail ---

chiayewken/blocksworld

--- dataset_info: features: - name: domain dtype: string - name: instance dtype: string splits: - name: train num_bytes: 675434 num_examples: 501 download_size: 61032 dataset_size: 675434 configs: - config_name: default data_files: - split: train path: data/train-* --- # BlocksWorld This repo contains the BlocksWorld data for ["PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change"](https://arxiv.org/abs/2206.10498). The original data link is here: https://github.com/karthikv792/LLMs-Planning/tree/main/plan-bench/instances/blocksworld/generated

d0rj/rudetoxifier_data

--- dataset_info: features: - name: text dtype: string - name: toxic dtype: float64 splits: - name: train num_bytes: 27459998 num_examples: 163187 - name: test num_bytes: 1762288 num_examples: 10000 download_size: 16406619 dataset_size: 29222286 license: mit task_categories: - text-classification - text2text-generation language: - ru multilinguality: - monolingual tags: - toxicity - style-transfer pretty_name: RuDetoxifier data size_categories: - 100K<n<1M source_datasets: - original paperswithcode_id: methods-for-detoxification-of-texts-for-the --- # rudetoxifier_data ## Dataset Description - **Homepage:** https://github.com/s-nlp/rudetoxifier - **Repository:** https://github.com/s-nlp/rudetoxifier - **Paper:** [Methods for Detoxification of Texts for the Russian Language](https://arxiv.org/abs/2105.09052) - **Point of Contact:** [Daryna Dementieva](mailto:daryna.dementieva@skoltech.ru) Huggingface copy of Github repo with dataset.

EdwardLin2023/MELD-Audio

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

lucadiliello/relationextractionqa

--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers sequence: string - name: key dtype: string - name: labels list: - name: end sequence: int64 - name: start sequence: int64 splits: - name: test num_bytes: 863192 num_examples: 2948 download_size: 527428 dataset_size: 863192 --- # Dataset Card for "relationextractionqa" Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).

datasets-examples/doc-image-10

--- size_categories: - n<1K --- # [doc] image dataset 10 This dataset contains a parquet file that contains an image column.

avermeersch/calabi-yau-threefolds

--- name: Reflexive Polyhedra of Calabi-Yau Threefolds date: '2023-09-28' license: other domain: Physics tags: - Calabi-Yau - Toric Geometry - String Theory - Polyhedra - Geometry - Physics pretty_name: Calabi-Yau 3-Folds size_categories: - 1K<n<10K --- # Dataset Card for Reflexive Polyhedra of Calabi-Yau Threefolds ## Table of Contents - [Dataset Description](#dataset-description) - [General Information](#general-information) - [Dataset Origin](#dataset-origin) - [Dataset Characteristics](#dataset-characteristics) - [Schema](#schema) - [Data Fields](#data-fields) - [Data Format](#data-format) - [Usage](#usage) - [Getting Started](#getting-started) - [Machine Learning Applications](#machine-learning-applications) - [Citations](#citations) ## Dataset Description ### General Information Calabi-Yau threefolds are a special class of smooth, compact three-dimensional spaces that have become fundamental objects in both mathematics and theoretical physics. In the context of string theory, they serve as the internal dimensions over which strings compactify, leading to a four-dimensional effective theory. The geometry of these threefolds is closely related to many physical phenomena, including the number of particle generations, gauge symmetries, and the cosmological constant. This dataset encompasses all 4319 reflexive polyhedra in 3 dimensions, offering a comprehensive view of potential Calabi-Yau geometries. The reflexive polyhedra serve as dual representations of these threefolds and are crucial in understanding their topological and geometric properties. ### Dataset Origin The dataset is derived from the original work documented in [hep-th/9805190](https://arxiv.org/abs/hep-th/9805190). While the original dataset was in a PALP-compatible structure, this version has been converted to a nested JSON format to better accommodate machine learning applications. The PALP-compatible version of the dataset can be accessed at [CYk3](http://hep.itp.tuwien.ac.at/~kreuzer/CY/CYk3.html). ## Dataset Characteristics ### Schema The dataset is presented in a nested JSON format, with each entry containing both metadata and a matrix representing the vertices of the corresponding polyhedron. ### Data Fields - `M1`, `M2`: These are point and vertex numbers in the M lattice, which is a mathematical lattice in the context of toric geometry. This lattice serves as the foundational geometric space from which the polyhedron is constructed. - `N1`, `N2`: Similar to the M lattice, these are point and vertex numbers in the N lattice, which is dual to the M lattice. The N lattice provides a different but equally important geometric perspective for understanding the polyhedron. - `Pic`: The Picard number is a topological invariant that measures the rank of the Néron-Severi group of a manifold. In the context of Calabi-Yau threefolds, it helps to determine the number of independent 2-cycles, which has physical implications like the number of U(1) gauge fields in the effective theory. - `Cor`: The correction term is a specific mathematical entity that adjusts the Picard number to account for certain topological peculiarities. The Picard numbers of a polyhedron and its dual add up to \( 20 + \text{correction} \). - `Matrix`: A 3xN matrix containing the coordinates of the vertices of the polyhedron. Each row represents a dimension in 3D space, and each column represents a vertex. ### Data Format Each entry in the dataset is structured as follows: ```json { "M1": ..., "M2": ..., "N1": ..., "N2": ..., "Pic": ..., "Cor": ..., "Matrix": [ [...], [...], [...] ] } ``` ## Usage ### Getting Started To access the dataset using the Hugging Face `datasets` library, the following Python code can be used: ```python from datasets import load_dataset dataset = load_dataset("calabi-yau-threefolds") ``` ### Machine Learning Applications This dataset provides rich opportunities for various machine learning tasks: - Geometric deep learning for topological invariant prediction. - Unsupervised learning techniques for polyhedra clustering. - Graph neural networks to model vertex connections. ### Citations For dataset usage, please cite the original paper using the following BibTeX entry: ```bibtex @misc{kreuzer1998classification, title={Classification of Reflexive Polyhedra in Three Dimensions}, author={M. Kreuzer and H. Skarke}, year={1998}, eprint={hep-th/9805190}, archivePrefix={arXiv}, primaryClass={hep-th} } ```

heliosprime/twitter_dataset_1713182160

--- 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: 19353 num_examples: 51 download_size: 18944 dataset_size: 19353 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713182160" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

anan-2024/twitter_dataset_1712985523

--- 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: 20835 num_examples: 45 download_size: 11521 dataset_size: 20835 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/iyo_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of iyo/壱与/壹与 (Fate/Grand Order) This is the dataset of iyo/壱与/壹与 (Fate/Grand Order), containing 37 images and their tags. The core tags of this character are `long_hair, twintails, breasts, brown_hair, parted_bangs, large_breasts, very_long_hair, brown_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 | 37 | 52.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iyo_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 37 | 44.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iyo_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 89 | 84.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iyo_fgo/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/iyo_fgo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](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, bare_shoulders, blush, grey_dress, sash, body_markings, sideboob, solo, white_dress, looking_at_viewer, smile, open_mouth, purple_eyes, small_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | blush | grey_dress | sash | body_markings | sideboob | solo | white_dress | looking_at_viewer | smile | open_mouth | purple_eyes | small_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:-------------|:-------|:----------------|:-----------|:-------|:--------------|:--------------------|:--------|:-------------|:--------------|:----------------| | 0 | 10 | ![](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 |

Seongill/Trivia_missing_10_small

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 25984921 num_examples: 3771 download_size: 15465970 dataset_size: 25984921 configs: - config_name: default data_files: - split: train path: data/train-* ---

carloswylker/batista_forro

--- license: openrail ---

graphs-datasets/AIDS

--- licence: unknown task_categories: - graph-ml --- # Dataset Card for AIDS ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [External Use](#external-use) - [PyGeometric](#pygeometric) - [Dataset Structure](#dataset-structure) - [Data Properties](#data-properties) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **[Homepage](https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data)** - **Paper:**: (see citation) - **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-aids) ### Dataset Summary The `AIDS` dataset is a dataset containing compounds checked for evidence of anti-HIV activity.. ### Supported Tasks and Leaderboards `AIDS` should be used for molecular classification, a binary classification task. The score used is accuracy with cross validation. ## External Use ### PyGeometric To load in PyGeometric, do the following: ```python from datasets import load_dataset from torch_geometric.data import Data from torch_geometric.loader import DataLoader dataset_hf = load_dataset("graphs-datasets/<mydataset>") # For the train set (replace by valid or test as needed) dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] dataset_pg = DataLoader(dataset_pg_list) ``` ## Dataset Structure ### Data Properties | property | value | |---|---| | scale | medium | | #graphs | 1999 | | average #nodes | 15.5875 | | average #edges | 32.39 | ### Data Fields Each row of a given file is a graph, with: - `node_feat` (list: #nodes x #node-features): nodes - `edge_index` (list: 2 x #edges): pairs of nodes constituting edges - `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features - `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one) - `num_nodes` (int): number of nodes of the graph ### Data Splits This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset. ## Additional Information ### Licensing Information The dataset has been released under license unknown. ### Citation Information ``` @inproceedings{Morris+2020, title={TUDataset: A collection of benchmark datasets for learning with graphs}, author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann}, booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)}, archivePrefix={arXiv}, eprint={2007.08663}, url={www.graphlearning.io}, year={2020} } ``` ``` @InProceedings{10.1007/978-3-540-89689-0_33, author="Riesen, Kaspar and Bunke, Horst", editor="da Vitoria Lobo, Niels and Kasparis, Takis and Roli, Fabio and Kwok, James T. and Georgiopoulos, Michael and Anagnostopoulos, Georgios C. and Loog, Marco", title="IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning", booktitle="Structural, Syntactic, and Statistical Pattern Recognition", year="2008", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="287--297", abstract="In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.", isbn="978-3-540-89689-0" } ```