davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
LuangMV97/EmpathetiCounseling_Previo	--- dataset_info: features: - name: input dtype: string - name: label dtype: string splits: - name: train num_bytes: 13883804.45384577 num_examples: 47998 - name: test num_bytes: 5771364.976021614 num_examples: 12002 download_size: 13237318 dataset_size: 19655169.429867383 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
cjvt/sentinews	--- annotations_creators: - crowdsourced language: - sl language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: SentiNews size_categories: [] source_datasets: - original tags: - slovenian sentiment - news articles task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for SentiNews ## Dataset Description - Homepage: https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene - Paper: Bučar, J., Žnidaršič, M. & Povh, J. Annotated news corpora and a lexicon for sentiment analysis in Slovene. Lang Resources & Evaluation 52, 895–919 (2018). https://doi.org/10.1007/s10579-018-9413-3 ### Dataset Summary SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their sentiment by between two and six annotators. It is annotated at three granularities: - document-level (config `document_level`, 10 427 documents), - paragraph-level (config `paragraph_level`, 89 999 paragraphs), and - sentence-level (config `sentence_level`, 168 899 sentences). ### Supported Tasks and Leaderboards Sentiment classification, three classes (negative, neutral, positive). ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the sentence-level config: ``` { 'nid': 2, 'content': 'Vilo Prešeren je na dražbi ministrstva za obrambo kupilo nepremičninsko podjetje Condor Real s sedežem v Lescah.', 'sentiment': 'neutral', 'pid': 1, 'sid': 1 } ``` ### Data Fields The data fields are similar among all three configs, with the only difference being the IDs. - `nid`: a uint16 containing a unique ID of the news article (document). - `content`: a string containing the body of the news article - `sentiment`: the sentiment of the instance - `pid`: a uint8 containing the consecutive number of the paragraph inside the current news article, not unique (present in the configs `paragraph_level` and `sentence_level`) - `sid`: a uint8 containing the consecutive number of the sentence inside the current paragraph, not unique (present in the config `sentence_level`) ## Additional Information ### Dataset Curators Jože Bučar, Martin Žnidaršič, Janez Povh. ### Licensing Information CC BY-SA 4.0 ### Citation Information ``` @article{buvcar2018annotated, title={Annotated news corpora and a lexicon for sentiment analysis in Slovene}, author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez}, journal={Language Resources and Evaluation}, volume={52}, number={3}, pages={895--919}, year={2018}, publisher={Springer} } ``` ### Contributions Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
TheFinAI/flare-fomc	--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: text dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: test num_bytes: 384180 num_examples: 496 download_size: 140144 dataset_size: 384180 --- # Dataset Card for "flare-fomc" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
r0ll/kussia	--- license: openrail language: - ru --- Voice https://www.twitch.tv/kussia88 RVC v2 350 epoch
open-llm-leaderboard/details_smelborp__MixtralOrochi8x7B	--- pretty_name: Evaluation run of smelborp/MixtralOrochi8x7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B)\ \ 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_smelborp__MixtralOrochi8x7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-30T02:24:04.286101](https://huggingface.co/datasets/open-llm-leaderboard/details_smelborp__MixtralOrochi8x7B/blob/main/results_2023-12-30T02-24-04.286101.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\": 0.6933650457336848,\n\ \ \"acc_stderr\": 0.030427906094644037,\n \"acc_norm\": 0.7040806942153237,\n\ \ \"acc_norm_stderr\": 0.03106416581410797,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6399397085586839,\n\ \ \"mc2_stderr\": 0.015220747814252549\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6655290102389079,\n \"acc_stderr\": 0.013787460322441379,\n\ \ \"acc_norm\": 0.7030716723549488,\n \"acc_norm_stderr\": 0.013352025976725225\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6814379605656243,\n\ \ \"acc_stderr\": 0.00464966527389064,\n \"acc_norm\": 0.8609838677554272,\n\ \ \"acc_norm_stderr\": 0.0034525630964691227\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742399,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742399\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7697368421052632,\n \"acc_stderr\": 0.03426059424403165,\n\ \ \"acc_norm\": 0.7697368421052632,\n \"acc_norm_stderr\": 0.03426059424403165\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7886792452830189,\n \"acc_stderr\": 0.02512576648482785,\n\ \ \"acc_norm\": 0.7886792452830189,\n \"acc_norm_stderr\": 0.02512576648482785\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565656,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565656\n },\n \"harness\|hendrycksTest-college_chemistry\|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_computer_science\|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-college_mathematics\|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_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n\ \ \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7021276595744681,\n \"acc_stderr\": 0.02989614568209546,\n\ \ \"acc_norm\": 0.7021276595744681,\n \"acc_norm_stderr\": 0.02989614568209546\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5526315789473685,\n\ \ \"acc_stderr\": 0.046774730044912,\n \"acc_norm\": 0.5526315789473685,\n\ \ \"acc_norm_stderr\": 0.046774730044912\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n\ \ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.5211640211640212,\n \"acc_stderr\": 0.025728230952130723,\n \"\ acc_norm\": 0.5211640211640212,\n \"acc_norm_stderr\": 0.025728230952130723\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5317460317460317,\n\ \ \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.5317460317460317,\n\ \ \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8387096774193549,\n \"acc_stderr\": 0.020923327006423298,\n \"\ acc_norm\": 0.8387096774193549,\n \"acc_norm_stderr\": 0.020923327006423298\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5960591133004927,\n \"acc_stderr\": 0.03452453903822032,\n \"\ acc_norm\": 0.5960591133004927,\n \"acc_norm_stderr\": 0.03452453903822032\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8535353535353535,\n \"acc_stderr\": 0.025190921114603918,\n \"\ acc_norm\": 0.8535353535353535,\n \"acc_norm_stderr\": 0.025190921114603918\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9481865284974094,\n \"acc_stderr\": 0.01599622932024412,\n\ \ \"acc_norm\": 0.9481865284974094,\n \"acc_norm_stderr\": 0.01599622932024412\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8025210084033614,\n \"acc_stderr\": 0.025859164122051453,\n\ \ \"acc_norm\": 0.8025210084033614,\n \"acc_norm_stderr\": 0.025859164122051453\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4370860927152318,\n \"acc_stderr\": 0.04050035722230636,\n \"\ acc_norm\": 0.4370860927152318,\n \"acc_norm_stderr\": 0.04050035722230636\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8807339449541285,\n \"acc_stderr\": 0.013895729292588957,\n \"\ acc_norm\": 0.8807339449541285,\n \"acc_norm_stderr\": 0.013895729292588957\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5694444444444444,\n \"acc_stderr\": 0.03376922151252335,\n \"\ acc_norm\": 0.5694444444444444,\n \"acc_norm_stderr\": 0.03376922151252335\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8725490196078431,\n \"acc_stderr\": 0.023405530480846315,\n \"\ acc_norm\": 0.8725490196078431,\n \"acc_norm_stderr\": 0.023405530480846315\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n \ \ \"acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7668161434977578,\n\ \ \"acc_stderr\": 0.02838039114709471,\n \"acc_norm\": 0.7668161434977578,\n\ \ \"acc_norm_stderr\": 0.02838039114709471\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525975,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525975\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622793,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622793\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.03083349114628123,\n\ \ \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.03083349114628123\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5267857142857143,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9145299145299145,\n\ \ \"acc_stderr\": 0.01831589168562586,\n \"acc_norm\": 0.9145299145299145,\n\ \ \"acc_norm_stderr\": 0.01831589168562586\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909282\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8722860791826309,\n\ \ \"acc_stderr\": 0.011935626313999874,\n \"acc_norm\": 0.8722860791826309,\n\ \ \"acc_norm_stderr\": 0.011935626313999874\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044283,\n\ \ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044283\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.464804469273743,\n\ \ \"acc_stderr\": 0.016681020931076655,\n \"acc_norm\": 0.464804469273743,\n\ \ \"acc_norm_stderr\": 0.016681020931076655\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.0231527224394023,\n\ \ \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.0231527224394023\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8117283950617284,\n \"acc_stderr\": 0.021751866060815875,\n\ \ \"acc_norm\": 0.8117283950617284,\n \"acc_norm_stderr\": 0.021751866060815875\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5212765957446809,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.5212765957446809,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5260756192959583,\n\ \ \"acc_stderr\": 0.012752858346533143,\n \"acc_norm\": 0.5260756192959583,\n\ \ \"acc_norm_stderr\": 0.012752858346533143\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.025187786660227255,\n\ \ \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.025187786660227255\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7369281045751634,\n \"acc_stderr\": 0.017812676542320657,\n \ \ \"acc_norm\": 0.7369281045751634,\n \"acc_norm_stderr\": 0.017812676542320657\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8040816326530612,\n \"acc_stderr\": 0.025409301953225678,\n\ \ \"acc_norm\": 0.8040816326530612,\n \"acc_norm_stderr\": 0.025409301953225678\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.032659863237109066,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.032659863237109066\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072878,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072878\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6399397085586839,\n\ \ \"mc2_stderr\": 0.015220747814252549\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7987371744277821,\n \"acc_stderr\": 0.011268519971577682\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.1728582259287339,\n \ \ \"acc_stderr\": 0.010415432246200583\n }\n}\n```" repo_url: https://huggingface.co/smelborp/MixtralOrochi8x7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|arc:challenge\|25_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-30T02-24-04.286101.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|gsm8k\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hellaswag\|10_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T02-24-04.286101.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-30T02-24-04.286101.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '/details_harness\|winogrande\|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-30T02-24-04.286101.parquet' - config_name: results data_files: - split: 2023_12_30T02_24_04.286101 path: - results_2023-12-30T02-24-04.286101.parquet - split: latest path: - results_2023-12-30T02-24-04.286101.parquet --- # Dataset Card for Evaluation run of smelborp/MixtralOrochi8x7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B) 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_smelborp__MixtralOrochi8x7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-30T02:24:04.286101](https://huggingface.co/datasets/open-llm-leaderboard/details_smelborp__MixtralOrochi8x7B/blob/main/results_2023-12-30T02-24-04.286101.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": 0.6933650457336848, "acc_stderr": 0.030427906094644037, "acc_norm": 0.7040806942153237, "acc_norm_stderr": 0.03106416581410797, "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6399397085586839, "mc2_stderr": 0.015220747814252549 }, "harness\|arc:challenge\|25": { "acc": 0.6655290102389079, "acc_stderr": 0.013787460322441379, "acc_norm": 0.7030716723549488, "acc_norm_stderr": 0.013352025976725225 }, "harness\|hellaswag\|10": { "acc": 0.6814379605656243, "acc_stderr": 0.00464966527389064, "acc_norm": 0.8609838677554272, "acc_norm_stderr": 0.0034525630964691227 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742399, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742399 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7697368421052632, "acc_stderr": 0.03426059424403165, "acc_norm": 0.7697368421052632, "acc_norm_stderr": 0.03426059424403165 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7886792452830189, "acc_stderr": 0.02512576648482785, "acc_norm": 0.7886792452830189, "acc_norm_stderr": 0.02512576648482785 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565656, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565656 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7021276595744681, "acc_stderr": 0.02989614568209546, "acc_norm": 0.7021276595744681, "acc_norm_stderr": 0.02989614568209546 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5526315789473685, "acc_stderr": 0.046774730044912, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.046774730044912 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419036, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419036 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.5211640211640212, "acc_stderr": 0.025728230952130723, "acc_norm": 0.5211640211640212, "acc_norm_stderr": 0.025728230952130723 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5317460317460317, "acc_stderr": 0.04463112720677173, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677173 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8387096774193549, "acc_stderr": 0.020923327006423298, "acc_norm": 0.8387096774193549, "acc_norm_stderr": 0.020923327006423298 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5960591133004927, "acc_stderr": 0.03452453903822032, "acc_norm": 0.5960591133004927, "acc_norm_stderr": 0.03452453903822032 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8535353535353535, "acc_stderr": 0.025190921114603918, "acc_norm": 0.8535353535353535, "acc_norm_stderr": 0.025190921114603918 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9481865284974094, "acc_stderr": 0.01599622932024412, "acc_norm": 0.9481865284974094, "acc_norm_stderr": 0.01599622932024412 }, 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"harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5694444444444444, "acc_stderr": 0.03376922151252335, "acc_norm": 0.5694444444444444, "acc_norm_stderr": 0.03376922151252335 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8725490196078431, "acc_stderr": 0.023405530480846315, "acc_norm": 0.8725490196078431, "acc_norm_stderr": 0.023405530480846315 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8734177215189873, "acc_stderr": 0.021644195727955173, "acc_norm": 0.8734177215189873, "acc_norm_stderr": 0.021644195727955173 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7668161434977578, "acc_stderr": 0.02838039114709471, "acc_norm": 0.7668161434977578, "acc_norm_stderr": 0.02838039114709471 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.034465133507525975, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.034465133507525975 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622793, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622793 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8240740740740741, "acc_stderr": 0.036809181416738807, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.036809181416738807 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8098159509202454, "acc_stderr": 0.03083349114628123, "acc_norm": 0.8098159509202454, "acc_norm_stderr": 0.03083349114628123 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5267857142857143, "acc_stderr": 0.047389751192741546, "acc_norm": 0.5267857142857143, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9145299145299145, "acc_stderr": 0.01831589168562586, "acc_norm": 0.9145299145299145, "acc_norm_stderr": 0.01831589168562586 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909282, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909282 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8722860791826309, "acc_stderr": 0.011935626313999874, "acc_norm": 0.8722860791826309, "acc_norm_stderr": 0.011935626313999874 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7427745664739884, "acc_stderr": 0.023532925431044283, "acc_norm": 0.7427745664739884, "acc_norm_stderr": 0.023532925431044283 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.464804469273743, "acc_stderr": 0.016681020931076655, "acc_norm": 0.464804469273743, "acc_norm_stderr": 0.016681020931076655 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7941176470588235, "acc_stderr": 0.0231527224394023, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.0231527224394023 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8117283950617284, "acc_stderr": 0.021751866060815875, "acc_norm": 0.8117283950617284, "acc_norm_stderr": 0.021751866060815875 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5212765957446809, "acc_stderr": 0.029800481645628693, "acc_norm": 0.5212765957446809, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5260756192959583, "acc_stderr": 0.012752858346533143, "acc_norm": 0.5260756192959583, "acc_norm_stderr": 0.012752858346533143 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7794117647058824, "acc_stderr": 0.025187786660227255, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.025187786660227255 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7369281045751634, "acc_stderr": 0.017812676542320657, "acc_norm": 0.7369281045751634, "acc_norm_stderr": 0.017812676542320657 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302505, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302505 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8040816326530612, "acc_stderr": 0.025409301953225678, "acc_norm": 0.8040816326530612, "acc_norm_stderr": 0.025409301953225678 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101706, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101706 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.032659863237109066, "acc_norm": 0.88, "acc_norm_stderr": 0.032659863237109066 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072878, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072878 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6399397085586839, "mc2_stderr": 0.015220747814252549 }, "harness\|winogrande\|5": { "acc": 0.7987371744277821, "acc_stderr": 0.011268519971577682 }, "harness\|gsm8k\|5": { "acc": 0.1728582259287339, "acc_stderr": 0.010415432246200583 } } ``` ## 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 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Athiwat/brain2	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 5590698.0 num_examples: 5 download_size: 5591635 dataset_size: 5590698.0 --- # Dataset Card for "brain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davidgaofc/d_shadow_inout	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 927049 num_examples: 3280 download_size: 371018 dataset_size: 927049 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/shiki_eiki_touhou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of shiki_eiki/四季映姫・ヤマザナドゥ/四季映姫/시키에이키야마자나두 (Touhou) This is the dataset of shiki_eiki/四季映姫・ヤマザナドゥ/四季映姫/시키에이키야마자나두 (Touhou), containing 500 images and their tags. The core tags of this character are `green_hair, hat, short_hair, blue_eyes, ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:------------\|:-------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 601.94 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 366.46 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1145 \| 750.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 543.28 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1145 \| 1018.10 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/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/shiki_eiki_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blue_vest, long_sleeves, solo, white_shirt, frilled_hat, looking_at_viewer, simple_background, bangs, epaulettes, holding, rod_of_remorse, white_background, blue_headwear, black_skirt, blush, bow, open_mouth, upper_body, closed_mouth \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_footwear, black_skirt, blue_vest, full_body, long_sleeves, ribbon-trimmed_skirt, rod_of_remorse, solo, white_shirt, asymmetrical_hair, bangs, blue_headwear, frilled_hat, holding, white_socks, closed_mouth, looking_at_viewer, red_bow, epaulettes, white_ribbon, footwear_bow, red_ribbon, standing, white_background, white_bow, simple_background, buttons, green_eyes, mary_janes \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bangs, black_skirt, blue_vest, cowboy_shot, juliet_sleeves, looking_at_viewer, red_ribbon, ribbon-trimmed_skirt, rod_of_remorse, solo, wide_sleeves, blush, closed_mouth, epaulettes, holding, white_shirt, frilled_hat, frilled_skirt, hair_between_eyes, red_bow, smile, white_ribbon, black_background, blue_headwear, green_eyes, hat_ribbon, spider_lily, standing \| \| 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, hat_ribbon, shirt, solo, vest, looking_at_viewer, rod_of_remorse, skirt, juliet_sleeves, spider_lily, open_mouth, petals, wide_sleeves \| \| 4 \| 7 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, black_thighhighs, rod_of_remorse, skirt, solo, wide_sleeves, hat_ribbon, long_sleeves, zettai_ryouiki, vest, green_eyes \| \| 5 \| 9 \| ![](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, solo, rod_of_remorse, upper_body, blush, looking_at_viewer \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, adapted_costume, black_thighhighs, detached_sleeves, skirt, solo, alternate_costume, bare_shoulders, blush, bow, looking_at_viewer, magical_girl, rod_of_remorse, smile, zettai_ryouiki, asymmetrical_hair, hat_ribbon, open_mouth, boots, frills \| \| 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) \| 1boy, 1girl, blush, hetero, mosaic_censoring, open_mouth, penis, sex, solo_focus, vaginal, bangs, blue_vest, cum_in_pussy, frilled_hat, nipples, on_back, white_shirt, blue_headwear, feet_out_of_frame, long_sleeves, looking_at_viewer, missionary, alternate_breast_size, black_skirt, bow, breast_grab, clothing_aside, cum_on_breasts, grabbing, hair_between_eyes, huge_breasts, navel, panties, pov, spread_legs \| \| 8 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, hetero, open_mouth, penis, sex, solo_focus, vaginal, nipples, 1boy, blush, cum_in_pussy, cowgirl_position, girl_on_top, navel, nude, bangs, flat_chest, frilled_hat, mosaic_censoring, sweat, bar_censor, hair_between_eyes, heart, large_breasts, looking_at_viewer, tears, thighhighs \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, blush, looking_at_viewer, navel, nipples, pussy, solo, standing, bangs, frilled_hat, open_mouth, simple_background, small_breasts, white_background, ass_visible_through_thighs, censored, completely_nude, asymmetrical_hair, collarbone, cowboy_shot, green_eyes, hair_between_eyes, red_ribbon, thigh_gap, white_ribbon \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blue_vest \| long_sleeves \| solo \| white_shirt \| frilled_hat \| looking_at_viewer \| simple_background \| bangs \| epaulettes \| holding \| rod_of_remorse \| white_background \| blue_headwear \| black_skirt \| blush \| bow \| open_mouth \| upper_body \| closed_mouth \| black_footwear \| full_body \| ribbon-trimmed_skirt \| asymmetrical_hair \| white_socks \| red_bow \| white_ribbon \| footwear_bow \| red_ribbon \| standing \| white_bow \| buttons \| green_eyes \| mary_janes \| cowboy_shot \| juliet_sleeves \| wide_sleeves \| frilled_skirt \| hair_between_eyes \| smile \| black_background \| hat_ribbon \| spider_lily \| shirt \| vest \| skirt \| petals \| black_thighhighs \| zettai_ryouiki \| adapted_costume \| detached_sleeves \| alternate_costume \| bare_shoulders \| magical_girl \| boots \| frills \| 1boy \| hetero \| mosaic_censoring \| penis \| sex \| solo_focus \| vaginal \| cum_in_pussy \| nipples \| on_back \| feet_out_of_frame \| missionary \| alternate_breast_size \| breast_grab \| clothing_aside \| cum_on_breasts \| grabbing \| huge_breasts \| navel \| panties \| pov \| spread_legs \| cowgirl_position \| girl_on_top \| nude \| flat_chest \| sweat \| bar_censor \| heart \| large_breasts \| tears \| thighhighs \| pussy \| small_breasts \| ass_visible_through_thighs \| censored \| completely_nude \| collarbone \| thigh_gap \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:------------\|:---------------\|:-------\|:--------------\|:--------------\|:--------------------\|:--------------------\|:--------\|:-------------\|:----------\|:-----------------\|:-------------------\|:----------------\|:--------------\|:--------\|:------\|:-------------\|:-------------\|:---------------\|:-----------------\|:------------\|:-----------------------\|:--------------------\|:--------------\|:----------\|:---------------\|:---------------\|:-------------\|:-----------\|:------------\|:----------\|:-------------\|:-------------\|:--------------\|:-----------------\|:---------------\|:----------------\|:--------------------\|:--------\|:-------------------\|:-------------\|:--------------\|:--------\|:-------\|:--------\|:---------\|:-------------------\|:-----------------\|:------------------\|:-------------------\|:--------------------\|:-----------------\|:---------------\|:--------\|:---------\|:-------\|:---------\|:-------------------\|:--------\|:------\|:-------------\|:----------\|:---------------\|:----------\|:----------\|:--------------------\|:-------------\|:------------------------\|:--------------\|:-----------------\|:-----------------\|:-----------\|:---------------\|:--------\|:----------\|:------\|:--------------\|:-------------------\|:--------------\|:-------\|:-------------\|:--------\|:-------------\|:--------\|:----------------\|:--------\|:-------------\|:--------\|:----------------\|:-----------------------------\|:-----------\|:------------------\|:-------------\|:------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 10 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| X \| X \| X \| \| \| \| X \| \| \| X \| \| \| X \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 7 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| X \| \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| \| \| X \| X \| \| X \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| \| X \| X \| X \| X \| \| \| \| X \| \| \| X \| \| X \| \| \| \| \| \| X \| \| \| X \| \| X \| X \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/9c69e716	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1342 dataset_size: 180 --- # Dataset Card for "9c69e716" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/cards20230512	--- dataset_info: features: - name: url dtype: string - name: card dtype: string splits: - name: train num_bytes: 264313043 num_examples: 202911 download_size: 78441708 dataset_size: 264313043 --- # Dataset Card for "cards20230512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chiyuanhsiao/ML2021_HungyiLee_Corpus	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: test num_bytes: 1086428751.563 num_examples: 31181 download_size: 1086479549 dataset_size: 1086428751.563 --- # Dataset Card for "debug" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
WarpWingHF/QTRAGPT	--- license: mit ---
sudopop/korean_food	--- license: unknown ---
LexiconShiftInnovations/SinhalaSubtitlesDatasetClean	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 51288853 num_examples: 1149 download_size: 20145527 dataset_size: 51288853 configs: - config_name: default data_files: - split: train path: data/train-* ---
ylacombe/bella_ciao	--- dataset_info: features: - name: audio dtype: audio - name: vocals dtype: audio - name: others dtype: audio splits: - name: train num_bytes: 158140620.0 num_examples: 30 download_size: 156971661 dataset_size: 158140620.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO	--- pretty_name: Evaluation run of Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO](https://huggingface.co/Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO)\ \ 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_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-11T12:02:04.707768](https://huggingface.co/datasets/open-llm-leaderboard/details_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO/blob/main/results_2024-02-11T12-02-04.707768.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\": 0.6083454936984162,\n\ \ \"acc_stderr\": 0.033140017189034275,\n \"acc_norm\": 0.6127945476017843,\n\ \ \"acc_norm_stderr\": 0.0338104933555728,\n \"mc1\": 0.40514075887392903,\n\ \ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5791139392635098,\n\ \ \"mc2_stderr\": 0.015266138543062658\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5665529010238908,\n \"acc_stderr\": 0.014481376224558903,\n\ \ \"acc_norm\": 0.6049488054607508,\n \"acc_norm_stderr\": 0.014285898292938163\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6436964748058156,\n\ \ \"acc_stderr\": 0.004779276329704048,\n \"acc_norm\": 0.8383788090021908,\n\ \ \"acc_norm_stderr\": 0.0036735065123709547\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n\ \ \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\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\"\ : 0.5,\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\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5780346820809249,\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\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.032529096196131965,\n\ \ \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.032529096196131965\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\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.38095238095238093,\n \"acc_stderr\": 0.0250107491161376,\n \"\ acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.0250107491161376\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.04343525428949097,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.04343525428949097\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-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\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.0356796977226805,\n\ \ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.0356796977226805\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646826,\n \"\ acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646826\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5717948717948718,\n \"acc_stderr\": 0.025088301454694827,\n\ \ \"acc_norm\": 0.5717948717948718,\n \"acc_norm_stderr\": 0.025088301454694827\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059288,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059288\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7981651376146789,\n \"acc_stderr\": 0.017208579357787586,\n \"\ acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.017208579357787586\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.7598039215686274,\n\ \ \"acc_stderr\": 0.02998373305591361,\n \"acc_norm\": 0.7598039215686274,\n\ \ \"acc_norm_stderr\": 0.02998373305591361\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.7552742616033755,\n \"acc_stderr\": 0.027985699387036423,\n\ \ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.027985699387036423\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6143497757847534,\n\ \ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n\ \ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.036959801280988226,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988226\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.04453197507374984,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.04453197507374984\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.03512385283705048,\n\ \ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.03512385283705048\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.045416094465039504,\n\ \ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.045416094465039504\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.02308663508684141,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.02308663508684141\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7803320561941252,\n\ \ \"acc_stderr\": 0.014805384478371155,\n \"acc_norm\": 0.7803320561941252,\n\ \ \"acc_norm_stderr\": 0.014805384478371155\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.024946792225272314,\n\ \ \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.024946792225272314\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3653631284916201,\n\ \ \"acc_stderr\": 0.01610483388014229,\n \"acc_norm\": 0.3653631284916201,\n\ \ \"acc_norm_stderr\": 0.01610483388014229\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.02633661346904663,\n\ \ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.02633661346904663\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6784565916398714,\n\ \ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\ \ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6851851851851852,\n \"acc_stderr\": 0.025842248700902168,\n\ \ \"acc_norm\": 0.6851851851851852,\n \"acc_norm_stderr\": 0.025842248700902168\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.029736592526424438,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.029736592526424438\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.44589308996088656,\n\ \ \"acc_stderr\": 0.012695244711379772,\n \"acc_norm\": 0.44589308996088656,\n\ \ \"acc_norm_stderr\": 0.012695244711379772\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5955882352941176,\n \"acc_stderr\": 0.029812630701569743,\n\ \ \"acc_norm\": 0.5955882352941176,\n \"acc_norm_stderr\": 0.029812630701569743\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6111111111111112,\n \"acc_stderr\": 0.019722058939618068,\n \ \ \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.019722058939618068\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7272727272727273,\n\ \ \"acc_stderr\": 0.04265792110940588,\n \"acc_norm\": 0.7272727272727273,\n\ \ \"acc_norm_stderr\": 0.04265792110940588\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\ \ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233257,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233257\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5,\n \ \ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \"\ acc_norm_stderr\": 0.03892494720807614\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.40514075887392903,\n\ \ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5791139392635098,\n\ \ \"mc2_stderr\": 0.015266138543062658\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836676\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.4177407126611069,\n \ \ \"acc_stderr\": 0.013584820638504832\n }\n}\n```" repo_url: https://huggingface.co/Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO 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_11T12_02_04.707768 path: - '/details_harness\|arc:challenge\|25_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-11T12-02-04.707768.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|gsm8k\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hellaswag\|10_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T12-02-04.707768.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-11T12-02-04.707768.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '/details_harness\|winogrande\|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-11T12-02-04.707768.parquet' - config_name: results data_files: - split: 2024_02_11T12_02_04.707768 path: - results_2024-02-11T12-02-04.707768.parquet - split: latest path: - results_2024-02-11T12-02-04.707768.parquet --- # Dataset Card for Evaluation run of Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO](https://huggingface.co/Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO) 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_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-11T12:02:04.707768](https://huggingface.co/datasets/open-llm-leaderboard/details_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO/blob/main/results_2024-02-11T12-02-04.707768.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": 0.6083454936984162, "acc_stderr": 0.033140017189034275, "acc_norm": 0.6127945476017843, "acc_norm_stderr": 0.0338104933555728, "mc1": 0.40514075887392903, "mc1_stderr": 0.01718561172775337, "mc2": 0.5791139392635098, "mc2_stderr": 0.015266138543062658 }, "harness\|arc:challenge\|25": { "acc": 0.5665529010238908, "acc_stderr": 0.014481376224558903, "acc_norm": 0.6049488054607508, "acc_norm_stderr": 0.014285898292938163 }, "harness\|hellaswag\|10": { "acc": 0.6436964748058156, "acc_stderr": 0.004779276329704048, "acc_norm": 0.8383788090021908, "acc_norm_stderr": 0.0036735065123709547 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.047609522856952365, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952365 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.631578947368421, "acc_stderr": 0.03925523381052932, "acc_norm": 0.631578947368421, "acc_norm_stderr": 0.03925523381052932 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "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": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.548936170212766, "acc_stderr": 0.032529096196131965, "acc_norm": 0.548936170212766, "acc_norm_stderr": 0.032529096196131965 }, "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": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.0250107491161376, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.0250107491161376 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949097, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949097 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "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": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.703030303030303, "acc_stderr": 0.0356796977226805, "acc_norm": 0.703030303030303, "acc_norm_stderr": 0.0356796977226805 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7424242424242424, "acc_stderr": 0.031156269519646826, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.031156269519646826 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8652849740932642, "acc_stderr": 0.02463978909770944, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.02463978909770944 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5717948717948718, "acc_stderr": 0.025088301454694827, "acc_norm": 0.5717948717948718, "acc_norm_stderr": 0.025088301454694827 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028597 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.031041941304059288, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059288 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.0386155754625517, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.0386155754625517 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7981651376146789, "acc_stderr": 0.017208579357787586, "acc_norm": 0.7981651376146789, "acc_norm_stderr": 0.017208579357787586 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7598039215686274, "acc_stderr": 0.02998373305591361, "acc_norm": 0.7598039215686274, "acc_norm_stderr": 0.02998373305591361 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7552742616033755, "acc_stderr": 0.027985699387036423, "acc_norm": 0.7552742616033755, "acc_norm_stderr": 0.027985699387036423 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6143497757847534, "acc_stderr": 0.03266842214289201, "acc_norm": 0.6143497757847534, "acc_norm_stderr": 0.03266842214289201 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.036959801280988226, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.036959801280988226 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.04453197507374984, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.04453197507374984 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7239263803680982, "acc_stderr": 0.03512385283705048, "acc_norm": 0.7239263803680982, "acc_norm_stderr": 0.03512385283705048 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6990291262135923, "acc_stderr": 0.045416094465039504, "acc_norm": 0.6990291262135923, "acc_norm_stderr": 0.045416094465039504 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8547008547008547, "acc_stderr": 0.02308663508684141, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.02308663508684141 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7803320561941252, "acc_stderr": 0.014805384478371155, "acc_norm": 0.7803320561941252, "acc_norm_stderr": 0.014805384478371155 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6878612716763006, "acc_stderr": 0.024946792225272314, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.024946792225272314 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3653631284916201, "acc_stderr": 0.01610483388014229, "acc_norm": 0.3653631284916201, "acc_norm_stderr": 0.01610483388014229 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.696078431372549, "acc_stderr": 0.02633661346904663, "acc_norm": 0.696078431372549, "acc_norm_stderr": 0.02633661346904663 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6851851851851852, "acc_stderr": 0.025842248700902168, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.025842248700902168 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46099290780141844, "acc_stderr": 0.029736592526424438, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.029736592526424438 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.44589308996088656, "acc_stderr": 0.012695244711379772, "acc_norm": 0.44589308996088656, "acc_norm_stderr": 0.012695244711379772 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5955882352941176, "acc_stderr": 0.029812630701569743, "acc_norm": 0.5955882352941176, "acc_norm_stderr": 0.029812630701569743 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.019722058939618068, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.019722058939618068 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940588, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940588 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065677, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065677 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233257, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233257 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727668, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727668 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.40514075887392903, "mc1_stderr": 0.01718561172775337, "mc2": 0.5791139392635098, "mc2_stderr": 0.015266138543062658 }, "harness\|winogrande\|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836676 }, "harness\|gsm8k\|5": { "acc": 0.4177407126611069, "acc_stderr": 0.013584820638504832 } } ``` ## 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]
bcui19/UltraMix	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 108959181 num_examples: 41457 download_size: 54577284 dataset_size: 108959181 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "UltraMix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vic0428/imdb-card-pred-scientific	--- dataset_info: features: - name: text dtype: string - name: prompt dtype: string - name: true_cardinality dtype: int64 splits: - name: train num_bytes: 39344995.2 num_examples: 80000 - name: test num_bytes: 9836248.8 num_examples: 20000 download_size: 8634654 dataset_size: 49181244.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "imdb-card-pred-scientific" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_adept__persimmon-8b-base	--- pretty_name: Evaluation run of adept/persimmon-8b-base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_adept__persimmon-8b-base\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-10-11T16:30:00.730198](https://huggingface.co/datasets/open-llm-leaderboard/details_adept__persimmon-8b-base/blob/main/results_2023-10-11T16-30-00.730198.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\": 0.4373382174928584,\n\ \ \"acc_stderr\": 0.03537473296886481,\n \"acc_norm\": 0.440779620602171,\n\ \ \"acc_norm_stderr\": 0.03536781150443019,\n \"mc1\": 0.22888616891064872,\n\ \ \"mc1_stderr\": 0.014706994909055027,\n \"mc2\": 0.378505315070287,\n\ \ \"mc2_stderr\": 0.013586954257578736\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.41552901023890787,\n \"acc_stderr\": 0.014401366641216384,\n\ \ \"acc_norm\": 0.4274744027303754,\n \"acc_norm_stderr\": 0.014456862944650652\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5203146783509262,\n\ \ \"acc_stderr\": 0.004985661282998582,\n \"acc_norm\": 0.7114120693089027,\n\ \ \"acc_norm_stderr\": 0.004521798577922143\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.4276315789473684,\n \"acc_stderr\": 0.04026097083296559,\n\ \ \"acc_norm\": 0.4276315789473684,\n \"acc_norm_stderr\": 0.04026097083296559\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.4377358490566038,\n \"acc_stderr\": 0.030533338430467512,\n\ \ \"acc_norm\": 0.4377358490566038,\n \"acc_norm_stderr\": 0.030533338430467512\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5208333333333334,\n\ \ \"acc_stderr\": 0.041775789507399935,\n \"acc_norm\": 0.5208333333333334,\n\ \ \"acc_norm_stderr\": 0.041775789507399935\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n\ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3988439306358382,\n\ \ \"acc_stderr\": 0.03733626655383509,\n \"acc_norm\": 0.3988439306358382,\n\ \ \"acc_norm_stderr\": 0.03733626655383509\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.03793281185307809,\n\ \ \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.03793281185307809\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.3617021276595745,\n \"acc_stderr\": 0.03141082197596241,\n\ \ \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.03141082197596241\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2804232804232804,\n \"acc_stderr\": 0.023135287974325642,\n \"\ acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.023135287974325642\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.4838709677419355,\n \"acc_stderr\": 0.028429203176724555,\n \"\ acc_norm\": 0.4838709677419355,\n \"acc_norm_stderr\": 0.028429203176724555\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.33004926108374383,\n \"acc_stderr\": 0.033085304262282574,\n \"\ acc_norm\": 0.33004926108374383,\n \"acc_norm_stderr\": 0.033085304262282574\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.5696969696969697,\n \"acc_stderr\": 0.03866225962879077,\n\ \ \"acc_norm\": 0.5696969696969697,\n \"acc_norm_stderr\": 0.03866225962879077\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.5050505050505051,\n \"acc_stderr\": 0.035621707606254015,\n \"\ acc_norm\": 0.5050505050505051,\n \"acc_norm_stderr\": 0.035621707606254015\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.5181347150259067,\n \"acc_stderr\": 0.036060650018329185,\n\ \ \"acc_norm\": 0.5181347150259067,\n \"acc_norm_stderr\": 0.036060650018329185\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.39487179487179486,\n \"acc_stderr\": 0.02478431694215638,\n\ \ \"acc_norm\": 0.39487179487179486,\n \"acc_norm_stderr\": 0.02478431694215638\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340492,\n \ \ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340492\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.39915966386554624,\n \"acc_stderr\": 0.031811100324139245,\n\ \ \"acc_norm\": 0.39915966386554624,\n \"acc_norm_stderr\": 0.031811100324139245\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119994,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119994\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.5321100917431193,\n \"acc_stderr\": 0.021393071222680797,\n \"\ acc_norm\": 0.5321100917431193,\n \"acc_norm_stderr\": 0.021393071222680797\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.2824074074074074,\n \"acc_stderr\": 0.03070137211151094,\n \"\ acc_norm\": 0.2824074074074074,\n \"acc_norm_stderr\": 0.03070137211151094\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.5882352941176471,\n \"acc_stderr\": 0.034542365853806094,\n \"\ acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.034542365853806094\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.5569620253164557,\n \"acc_stderr\": 0.032335327775334835,\n \ \ \"acc_norm\": 0.5569620253164557,\n \"acc_norm_stderr\": 0.032335327775334835\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.42152466367713004,\n\ \ \"acc_stderr\": 0.03314190222110657,\n \"acc_norm\": 0.42152466367713004,\n\ \ \"acc_norm_stderr\": 0.03314190222110657\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5419847328244275,\n \"acc_stderr\": 0.04369802690578756,\n\ \ \"acc_norm\": 0.5419847328244275,\n \"acc_norm_stderr\": 0.04369802690578756\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.5289256198347108,\n \"acc_stderr\": 0.04556710331269498,\n \"\ acc_norm\": 0.5289256198347108,\n \"acc_norm_stderr\": 0.04556710331269498\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.42592592592592593,\n\ \ \"acc_stderr\": 0.047803436269367894,\n \"acc_norm\": 0.42592592592592593,\n\ \ \"acc_norm_stderr\": 0.047803436269367894\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.588957055214724,\n \"acc_stderr\": 0.038656978537853624,\n\ \ \"acc_norm\": 0.588957055214724,\n \"acc_norm_stderr\": 0.038656978537853624\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3482142857142857,\n\ \ \"acc_stderr\": 0.04521829902833585,\n \"acc_norm\": 0.3482142857142857,\n\ \ \"acc_norm_stderr\": 0.04521829902833585\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.49514563106796117,\n \"acc_stderr\": 0.049505043821289195,\n\ \ \"acc_norm\": 0.49514563106796117,\n \"acc_norm_stderr\": 0.049505043821289195\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.6367521367521367,\n\ \ \"acc_stderr\": 0.03150712523091264,\n \"acc_norm\": 0.6367521367521367,\n\ \ \"acc_norm_stderr\": 0.03150712523091264\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.5466155810983397,\n\ \ \"acc_stderr\": 0.017802087135850304,\n \"acc_norm\": 0.5466155810983397,\n\ \ \"acc_norm_stderr\": 0.017802087135850304\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.4653179190751445,\n \"acc_stderr\": 0.0268542579282589,\n\ \ \"acc_norm\": 0.4653179190751445,\n \"acc_norm_stderr\": 0.0268542579282589\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24022346368715083,\n\ \ \"acc_stderr\": 0.014288343803925296,\n \"acc_norm\": 0.24022346368715083,\n\ \ \"acc_norm_stderr\": 0.014288343803925296\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.4869281045751634,\n \"acc_stderr\": 0.028620130800700246,\n\ \ \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.028620130800700246\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.4758842443729904,\n\ \ \"acc_stderr\": 0.028365041542564577,\n \"acc_norm\": 0.4758842443729904,\n\ \ \"acc_norm_stderr\": 0.028365041542564577\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.4660493827160494,\n \"acc_stderr\": 0.027756535257347666,\n\ \ \"acc_norm\": 0.4660493827160494,\n \"acc_norm_stderr\": 0.027756535257347666\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.35815602836879434,\n \"acc_stderr\": 0.028602085862759422,\n \ \ \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.028602085862759422\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3344198174706649,\n\ \ \"acc_stderr\": 0.012049668983214933,\n \"acc_norm\": 0.3344198174706649,\n\ \ \"acc_norm_stderr\": 0.012049668983214933\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.39705882352941174,\n \"acc_stderr\": 0.029722152099280058,\n\ \ \"acc_norm\": 0.39705882352941174,\n \"acc_norm_stderr\": 0.029722152099280058\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.38562091503267976,\n \"acc_stderr\": 0.01969145905235415,\n \ \ \"acc_norm\": 0.38562091503267976,\n \"acc_norm_stderr\": 0.01969145905235415\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5181818181818182,\n\ \ \"acc_stderr\": 0.04785964010794917,\n \"acc_norm\": 0.5181818181818182,\n\ \ \"acc_norm_stderr\": 0.04785964010794917\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.40408163265306124,\n \"acc_stderr\": 0.03141470802586589,\n\ \ \"acc_norm\": 0.40408163265306124,\n \"acc_norm_stderr\": 0.03141470802586589\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.572139303482587,\n\ \ \"acc_stderr\": 0.03498541988407795,\n \"acc_norm\": 0.572139303482587,\n\ \ \"acc_norm_stderr\": 0.03498541988407795\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.5964912280701754,\n \"acc_stderr\": 0.03762738699917057,\n\ \ \"acc_norm\": 0.5964912280701754,\n \"acc_norm_stderr\": 0.03762738699917057\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.22888616891064872,\n\ \ \"mc1_stderr\": 0.014706994909055027,\n \"mc2\": 0.378505315070287,\n\ \ \"mc2_stderr\": 0.013586954257578736\n }\n}\n```" repo_url: https://huggingface.co/adept/persimmon-8b-base leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|arc:challenge\|25_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hellaswag\|10_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-11T16-30-00.730198.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-management\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_11T16_30_00.730198 path: - '/details_harness\|truthfulqa:mc\|0_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-10-11T16-30-00.730198.parquet' - config_name: results data_files: - split: 2023_10_11T16_30_00.730198 path: - results_2023-10-11T16-30-00.730198.parquet - split: latest path: - results_2023-10-11T16-30-00.730198.parquet --- # Dataset Card for Evaluation run of adept/persimmon-8b-base ## Dataset Description - Homepage: - Repository: https://huggingface.co/adept/persimmon-8b-base - 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 [adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_adept__persimmon-8b-base", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-10-11T16:30:00.730198](https://huggingface.co/datasets/open-llm-leaderboard/details_adept__persimmon-8b-base/blob/main/results_2023-10-11T16-30-00.730198.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": 0.4373382174928584, "acc_stderr": 0.03537473296886481, "acc_norm": 0.440779620602171, "acc_norm_stderr": 0.03536781150443019, "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.378505315070287, "mc2_stderr": 0.013586954257578736 }, "harness\|arc:challenge\|25": { "acc": 0.41552901023890787, "acc_stderr": 0.014401366641216384, "acc_norm": 0.4274744027303754, "acc_norm_stderr": 0.014456862944650652 }, "harness\|hellaswag\|10": { "acc": 0.5203146783509262, "acc_stderr": 0.004985661282998582, "acc_norm": 0.7114120693089027, "acc_norm_stderr": 0.004521798577922143 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464242, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464242 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4276315789473684, "acc_stderr": 0.04026097083296559, "acc_norm": 0.4276315789473684, "acc_norm_stderr": 0.04026097083296559 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4377358490566038, "acc_stderr": 0.030533338430467512, "acc_norm": 0.4377358490566038, "acc_norm_stderr": 0.030533338430467512 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5208333333333334, "acc_stderr": 0.041775789507399935, "acc_norm": 0.5208333333333334, "acc_norm_stderr": 0.041775789507399935 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3988439306358382, "acc_stderr": 0.03733626655383509, "acc_norm": 0.3988439306358382, "acc_norm_stderr": 0.03733626655383509 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.17647058823529413, "acc_stderr": 0.03793281185307809, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.03793281185307809 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3617021276595745, "acc_stderr": 0.03141082197596241, "acc_norm": 0.3617021276595745, "acc_norm_stderr": 0.03141082197596241 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2804232804232804, "acc_stderr": 0.023135287974325642, "acc_norm": 0.2804232804232804, "acc_norm_stderr": 0.023135287974325642 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377563, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377563 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.4838709677419355, "acc_stderr": 0.028429203176724555, "acc_norm": 0.4838709677419355, "acc_norm_stderr": 0.028429203176724555 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.33004926108374383, "acc_stderr": 0.033085304262282574, "acc_norm": 0.33004926108374383, "acc_norm_stderr": 0.033085304262282574 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.5696969696969697, "acc_stderr": 0.03866225962879077, "acc_norm": 0.5696969696969697, "acc_norm_stderr": 0.03866225962879077 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5050505050505051, "acc_stderr": 0.035621707606254015, "acc_norm": 0.5050505050505051, "acc_norm_stderr": 0.035621707606254015 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.5181347150259067, "acc_stderr": 0.036060650018329185, "acc_norm": 0.5181347150259067, "acc_norm_stderr": 0.036060650018329185 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.39487179487179486, "acc_stderr": 0.02478431694215638, "acc_norm": 0.39487179487179486, "acc_norm_stderr": 0.02478431694215638 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2851851851851852, "acc_stderr": 0.027528599210340492, "acc_norm": 0.2851851851851852, "acc_norm_stderr": 0.027528599210340492 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.39915966386554624, "acc_stderr": 0.031811100324139245, "acc_norm": 0.39915966386554624, "acc_norm_stderr": 0.031811100324139245 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119994, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119994 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.5321100917431193, "acc_stderr": 0.021393071222680797, "acc_norm": 0.5321100917431193, "acc_norm_stderr": 0.021393071222680797 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.2824074074074074, "acc_stderr": 0.03070137211151094, "acc_norm": 0.2824074074074074, "acc_norm_stderr": 0.03070137211151094 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5882352941176471, "acc_stderr": 0.034542365853806094, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.034542365853806094 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5569620253164557, "acc_stderr": 0.032335327775334835, "acc_norm": 0.5569620253164557, "acc_norm_stderr": 0.032335327775334835 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.42152466367713004, "acc_stderr": 0.03314190222110657, "acc_norm": 0.42152466367713004, "acc_norm_stderr": 0.03314190222110657 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5419847328244275, "acc_stderr": 0.04369802690578756, "acc_norm": 0.5419847328244275, "acc_norm_stderr": 0.04369802690578756 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5289256198347108, "acc_stderr": 0.04556710331269498, "acc_norm": 0.5289256198347108, "acc_norm_stderr": 0.04556710331269498 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.42592592592592593, "acc_stderr": 0.047803436269367894, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.047803436269367894 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.588957055214724, "acc_stderr": 0.038656978537853624, "acc_norm": 0.588957055214724, "acc_norm_stderr": 0.038656978537853624 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3482142857142857, "acc_stderr": 0.04521829902833585, "acc_norm": 0.3482142857142857, "acc_norm_stderr": 0.04521829902833585 }, "harness\|hendrycksTest-management\|5": { "acc": 0.49514563106796117, "acc_stderr": 0.049505043821289195, "acc_norm": 0.49514563106796117, "acc_norm_stderr": 0.049505043821289195 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6367521367521367, "acc_stderr": 0.03150712523091264, "acc_norm": 0.6367521367521367, "acc_norm_stderr": 0.03150712523091264 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5466155810983397, "acc_stderr": 0.017802087135850304, "acc_norm": 0.5466155810983397, "acc_norm_stderr": 0.017802087135850304 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.4653179190751445, "acc_stderr": 0.0268542579282589, "acc_norm": 0.4653179190751445, "acc_norm_stderr": 0.0268542579282589 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24022346368715083, "acc_stderr": 0.014288343803925296, "acc_norm": 0.24022346368715083, "acc_norm_stderr": 0.014288343803925296 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.4869281045751634, "acc_stderr": 0.028620130800700246, "acc_norm": 0.4869281045751634, "acc_norm_stderr": 0.028620130800700246 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.4758842443729904, "acc_stderr": 0.028365041542564577, "acc_norm": 0.4758842443729904, "acc_norm_stderr": 0.028365041542564577 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.4660493827160494, "acc_stderr": 0.027756535257347666, "acc_norm": 0.4660493827160494, "acc_norm_stderr": 0.027756535257347666 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.35815602836879434, "acc_stderr": 0.028602085862759422, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.028602085862759422 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3344198174706649, "acc_stderr": 0.012049668983214933, "acc_norm": 0.3344198174706649, "acc_norm_stderr": 0.012049668983214933 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.39705882352941174, "acc_stderr": 0.029722152099280058, "acc_norm": 0.39705882352941174, "acc_norm_stderr": 0.029722152099280058 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.38562091503267976, "acc_stderr": 0.01969145905235415, "acc_norm": 0.38562091503267976, "acc_norm_stderr": 0.01969145905235415 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5181818181818182, "acc_stderr": 0.04785964010794917, "acc_norm": 0.5181818181818182, "acc_norm_stderr": 0.04785964010794917 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.40408163265306124, "acc_stderr": 0.03141470802586589, "acc_norm": 0.40408163265306124, "acc_norm_stderr": 0.03141470802586589 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.572139303482587, "acc_stderr": 0.03498541988407795, "acc_norm": 0.572139303482587, "acc_norm_stderr": 0.03498541988407795 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.5964912280701754, "acc_stderr": 0.03762738699917057, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.03762738699917057 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.378505315070287, "mc2_stderr": 0.013586954257578736 } } ``` ### 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]
tr416/dataset_20231006_200728	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 762696.0 num_examples: 297 - name: test num_bytes: 7704.0 num_examples: 3 download_size: 74080 dataset_size: 770400.0 --- # Dataset Card for "dataset_20231006_200728" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
c01dsnap/MaliciousPEs	--- license: other --- # Dataset Description Detailed description: [www.kaggle.com/competitions/malware-classification/overview/description](https://www.kaggle.com/competitions/malware-classification/overview/description) Warning: this dataset is almost half a terabyte uncompressed! We have compressed the data using 7zip to achieve the smallest file size possible. Note that the rules do not allow sharing of the data outside of Kaggle, including bit torrent ([why not?](https://www.kaggle.com/wiki/ANoteOnTorrents)). You are provided with a set of known malware files representing a mix of 9 different families. Each malware file has an Id, a 20 character hash value uniquely identifying the file, and a Class, an integer representing one of 9 family names to which the malware may belong: * Ramnit * Lollipop * Kelihos_ver3 * Vundo * Simda * Tracur * Kelihos_ver1 * Obfuscator.ACY * Gatak For each file, the raw data contains the hexadecimal representation of the file's binary content, without the PE header (to ensure sterility). You are also provided a metadata manifest, which is a log containing various metadata information extracted from the binary, such as function calls, strings, etc. This was generated using the IDA disassembler tool. Your task is to develop the best mechanism for classifying files in the test set into their respective family affiliations. The dataset contains the following files: * train.7z - the raw data for the training set (MD5 hash = 4fedb0899fc2210a6c843889a70952ed) * trainLabels.csv - the class labels associated with the training set * test.7z - the raw data for the test set (MD5 hash = 84b6fbfb9df3c461ed2cbbfa371ffb43) * sampleSubmission.csv - a file showing the valid submission format * dataSample.csv - a sample of the dataset to preview before downloading
EleutherAI/fake-cifarnet	--- dataset_info: features: - name: img dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck splits: - name: train num_bytes: 1827528011.0 num_examples: 190000 - name: test num_bytes: 96682029.0 num_examples: 10000 download_size: 1924310386 dataset_size: 1924210040.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- This is a dataset of "fake" CIFARNet images which were sampled from a high-entropy distribution whose mean and covariance matrix matches that of the original CIFARNet. It was generated with the following code: ```py from datasets import ClassLabel, Dataset, DatasetDict, Features, Image, load_dataset from functools import partial def generator(split: str): from datasets import Dataset from concept_erasure import assert_type, groupby, optimal_linear_shrinkage from concept_erasure.optimal_transport import psd_sqrt from PIL import Image as PilImage from torch import nn, optim, Tensor import torch def koleo(x: Tensor) -> Tensor: """Kozachenko-Leonenko estimator of entropy.""" return torch.cdist(x, x).kthvalue(2).values.log().mean() def hypercube_sample( n: int, mean: Tensor, cov: Tensor, , koleo_weight: float = 1e-3, max_iter: int = 100, seed: int = 0, ): """Generate `n` samples from a distribution on [0, 1]^d with the given moments.""" d = mean.shape[-1] assert d == cov.shape[-1] == cov.shape[-2], "Dimension mismatch" assert n > 1, "Need at least two samples to compute covariance" eps = torch.finfo(mean.dtype).eps rng = torch.Generator(device=mean.device).manual_seed(seed) # Initialize with max-ent samples matching `mean` and `cov` but without hypercube # constraint. We do so in a way that is robust to singular `cov` z = mean.new_empty([n, d]).normal_(generator=rng) x = torch.clamp(z @ psd_sqrt(cov) + mean, eps, 1 - eps) # Reparametrize to enforce hypercube constraint z = nn.Parameter(x.logit()) opt = optim.LBFGS([z], line_search_fn="strong_wolfe", max_iter=max_iter) def closure(): opt.zero_grad() x = z.sigmoid() loss = torch.norm(x.mean(0) - mean) + torch.norm(x.T.cov() - cov) loss -= koleo_weight koleo(x) loss.backward() return float(loss) opt.step(closure) return z.sigmoid().detach() ds = assert_type(Dataset, load_dataset("EleutherAI/cifarnet", split=split)) with ds.formatted_as("torch"): X = assert_type(Tensor, ds["image"]).div(255).cuda() Y = assert_type(Tensor, ds["label"]).cuda() # Iterate over the classes for y, x in groupby(X, Y): mean = x.flatten(1).mean(0) cov = optimal_linear_shrinkage(x.flatten(1).mT.cov(), len(x)) for fake_x in hypercube_sample(len(x), mean, cov).reshape_as(x).mul(255).cpu(): yield {"image": PilImage.fromarray(fake_x.numpy()).convert("RGB"), "label": y} features = Features({ "image": Image(), "label": ClassLabel(num_classes=10), }) fake_train = Dataset.from_generator(partial(generator, "train"), features) fake_test = Dataset.from_generator(partial(generator, "test"), features) fake = DatasetDict({"train": fake_train, "test": fake_test}) fake.push_to_hub("EleutherAI/fake-cifarnet") ```
LightFury9/ASR_mini1	--- dataset_info: features: - name: transcription dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 227312957.0 num_examples: 750 download_size: 186500925 dataset_size: 227312957.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
jmayank23/mini-platypus	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 6945089 num_examples: 3000 download_size: 3719748 dataset_size: 6945089 configs: - config_name: default data_files: - split: train path: data/train-* ---
NagendraHarish/mini-platypus	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245921 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_eldogbbhed__NeuralMonarchCoderPearlBeagle	--- pretty_name: Evaluation run of eldogbbhed/NeuralMonarchCoderPearlBeagle dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [eldogbbhed/NeuralMonarchCoderPearlBeagle](https://huggingface.co/eldogbbhed/NeuralMonarchCoderPearlBeagle)\ \ 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_eldogbbhed__NeuralMonarchCoderPearlBeagle\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-02T13:11:13.485539](https://huggingface.co/datasets/open-llm-leaderboard/details_eldogbbhed__NeuralMonarchCoderPearlBeagle/blob/main/results_2024-03-02T13-11-13.485539.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\": 0.6489879256686817,\n\ \ \"acc_stderr\": 0.03206960586209805,\n \"acc_norm\": 0.6497805245984531,\n\ \ \"acc_norm_stderr\": 0.03271953340550891,\n \"mc1\": 0.4565483476132191,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6119293421385199,\n\ \ \"mc2_stderr\": 0.015342412171122335\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620199,\n\ \ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.01357265770308495\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6900019916351324,\n\ \ \"acc_stderr\": 0.0046154722103160396,\n \"acc_norm\": 0.8722366062537343,\n\ \ \"acc_norm_stderr\": 0.0033314391934060397\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.032400380867927465,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.032400380867927465\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4649122807017544,\n\ \ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\ \ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6068965517241379,\n \"acc_stderr\": 0.0407032901370707,\n\ \ \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.0407032901370707\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"\ acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\"\ : 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\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\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n\ \ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047711\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026705,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026705\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097113,\n \ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097113\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524575,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524575\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887037,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887037\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931038,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931038\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.02636165166838909,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.02636165166838909\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.039418975265163025,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.039418975265163025\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.01987565502786746,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.01987565502786746\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8237547892720306,\n\ \ \"acc_stderr\": 0.013625556907993452,\n \"acc_norm\": 0.8237547892720306,\n\ \ \"acc_norm_stderr\": 0.013625556907993452\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069367,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069367\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.37318435754189944,\n\ \ \"acc_stderr\": 0.016175692013381957,\n \"acc_norm\": 0.37318435754189944,\n\ \ \"acc_norm_stderr\": 0.016175692013381957\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242557,\n\ \ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242557\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7234726688102894,\n\ \ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.7234726688102894,\n\ \ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303952,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303952\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170595,\n\ \ \"acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170595\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6617647058823529,\n \"acc_stderr\": 0.019139943748487036,\n \ \ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.019139943748487036\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712844,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712844\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578327,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578327\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4565483476132191,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6119293421385199,\n\ \ \"mc2_stderr\": 0.015342412171122335\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938282\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6702047005307051,\n \ \ \"acc_stderr\": 0.012949955030571154\n }\n}\n```" repo_url: https://huggingface.co/eldogbbhed/NeuralMonarchCoderPearlBeagle 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_03_02T13_11_13.485539 path: - '/details_harness\|arc:challenge\|25_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-02T13-11-13.485539.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|gsm8k\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hellaswag\|10_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T13-11-13.485539.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-02T13-11-13.485539.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '/details_harness\|winogrande\|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-02T13-11-13.485539.parquet' - config_name: results data_files: - split: 2024_03_02T13_11_13.485539 path: - results_2024-03-02T13-11-13.485539.parquet - split: latest path: - results_2024-03-02T13-11-13.485539.parquet --- # Dataset Card for Evaluation run of eldogbbhed/NeuralMonarchCoderPearlBeagle <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [eldogbbhed/NeuralMonarchCoderPearlBeagle](https://huggingface.co/eldogbbhed/NeuralMonarchCoderPearlBeagle) 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_eldogbbhed__NeuralMonarchCoderPearlBeagle", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-02T13:11:13.485539](https://huggingface.co/datasets/open-llm-leaderboard/details_eldogbbhed__NeuralMonarchCoderPearlBeagle/blob/main/results_2024-03-02T13-11-13.485539.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": 0.6489879256686817, "acc_stderr": 0.03206960586209805, "acc_norm": 0.6497805245984531, "acc_norm_stderr": 0.03271953340550891, "mc1": 0.4565483476132191, "mc1_stderr": 0.017437280953183688, "mc2": 0.6119293421385199, "mc2_stderr": 0.015342412171122335 }, "harness\|arc:challenge\|25": { "acc": 0.643344709897611, "acc_stderr": 0.013998056902620199, "acc_norm": 0.6851535836177475, "acc_norm_stderr": 0.01357265770308495 }, "harness\|hellaswag\|10": { "acc": 0.6900019916351324, "acc_stderr": 0.0046154722103160396, "acc_norm": 0.8722366062537343, "acc_norm_stderr": 0.0033314391934060397 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493864, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493864 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.0358687928008034, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.0358687928008034 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5659574468085107, "acc_stderr": 0.032400380867927465, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.032400380867927465 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6068965517241379, "acc_stderr": 0.0407032901370707, "acc_norm": 0.6068965517241379, "acc_norm_stderr": 0.0407032901370707 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.025197101074246483, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.025197101074246483 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.541871921182266, "acc_stderr": 0.03505630140785741, "acc_norm": 0.541871921182266, "acc_norm_stderr": 0.03505630140785741 }, "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": 0.793939393939394, "acc_stderr": 0.03158415324047711, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.03158415324047711 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.02886977846026705, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026705 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709443, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6692307692307692, "acc_stderr": 0.02385479568097113, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.02385479568097113 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524575, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524575 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887037, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887037 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461763, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461763 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5370370370370371, "acc_stderr": 0.03400603625538272, "acc_norm": 0.5370370370370371, "acc_norm_stderr": 0.03400603625538272 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931038, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931038 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7932489451476793, "acc_stderr": 0.02636165166838909, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.02636165166838909 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.035477710041594654, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.035477710041594654 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7520661157024794, "acc_stderr": 0.039418975265163025, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.039418975265163025 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573483, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573483 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7484662576687117, "acc_stderr": 0.034089978868575295, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.034089978868575295 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8974358974358975, "acc_stderr": 0.01987565502786746, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.01987565502786746 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993452, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993452 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069367, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069367 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.37318435754189944, "acc_stderr": 0.016175692013381957, "acc_norm": 0.37318435754189944, "acc_norm_stderr": 0.016175692013381957 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7234726688102894, "acc_stderr": 0.02540383297817961, "acc_norm": 0.7234726688102894, "acc_norm_stderr": 0.02540383297817961 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.024922001168886335, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.024922001168886335 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303952, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303952 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6948529411764706, "acc_stderr": 0.027971541370170595, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.027971541370170595 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6617647058823529, "acc_stderr": 0.019139943748487036, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.019139943748487036 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.726530612244898, "acc_stderr": 0.02853556033712844, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712844 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578327, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578327 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4565483476132191, "mc1_stderr": 0.017437280953183688, "mc2": 0.6119293421385199, "mc2_stderr": 0.015342412171122335 }, "harness\|winogrande\|5": { "acc": 0.8050513022888713, "acc_stderr": 0.011134099415938282 }, "harness\|gsm8k\|5": { "acc": 0.6702047005307051, "acc_stderr": 0.012949955030571154 } } ``` ## 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]
liuyanchen1015/MULTI_VALUE_stsb_regularized_plurals	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 29020 num_examples: 185 - name: test num_bytes: 22752 num_examples: 156 - name: train num_bytes: 91941 num_examples: 572 download_size: 95623 dataset_size: 143713 --- # Dataset Card for "MULTI_VALUE_stsb_regularized_plurals" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Har11k/demotrain1	--- license: apache-2.0 task_categories: - tabular-classification language: - en ---
CyberHarem/craven_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of craven/クレイヴン/克雷文 (Azur Lane) This is the dataset of craven/クレイヴン/克雷文 (Azur Lane), containing 17 images and their tags. The core tags of this character are `long_hair, purple_hair, drill_hair, yellow_eyes, bangs, breasts, 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 \| 17 \| 14.76 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 17 \| 10.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 36 \| 20.62 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 17 \| 13.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 36 \| 25.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/craven_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 17 \| ![](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, smile, looking_at_viewer, solo, open_mouth, white_thighhighs, navel, pleated_skirt, full_body, sailor_collar, school_uniform, shirt, shoes, standing, cheerleader, collarbone, long_sleeves, midriff, one_eye_closed, pom_pom_(cheerleading), white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| smile \| looking_at_viewer \| solo \| open_mouth \| white_thighhighs \| navel \| pleated_skirt \| full_body \| sailor_collar \| school_uniform \| shirt \| shoes \| standing \| cheerleader \| collarbone \| long_sleeves \| midriff \| one_eye_closed \| pom_pom_(cheerleading) \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:--------\|:--------------------\|:-------\|:-------------\|:-------------------\|:--------\|:----------------\|:------------\|:----------------\|:-----------------\|:--------\|:--------\|:-----------\|:--------------\|:-------------\|:---------------\|:----------\|:-----------------\|:-------------------------\|:-------------------\| \| 0 \| 17 \| ![](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 \|
ramixpe/sp_llama_format	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 20479775 num_examples: 20537 download_size: 4297673 dataset_size: 20479775 configs: - config_name: default data_files: - split: train path: data/train-* ---
Zakay/kuririn	--- license: openrail ---
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot-mathema-f8e841-1882064209	--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_cot eval_info: task: text_zero_shot_classification model: ArthurZ/opt-350m metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_cot dataset_config: mathemakitten--winobias_antistereotype_test_cot dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: ArthurZ/opt-350m * Dataset: mathemakitten/winobias_antistereotype_test_cot * Config: mathemakitten--winobias_antistereotype_test_cot * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
EleutherAI/lambada_openai	--- pretty_name: LAMBADA OpenAI language_creators: - machine-generated license: mit multilinguality: - translation task_ids: - language-modeling source_datasets: - lambada size_categories: - 1K<n<10K language: - de - en - es - fr - it dataset_info: - config_name: default features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: de features: - name: text dtype: string splits: - name: test num_bytes: 1904576 num_examples: 5153 download_size: 1985231 dataset_size: 1904576 - config_name: en features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: es features: - name: text dtype: string splits: - name: test num_bytes: 1821735 num_examples: 5153 download_size: 1902349 dataset_size: 1821735 - config_name: fr features: - name: text dtype: string splits: - name: test num_bytes: 1948795 num_examples: 5153 download_size: 2028703 dataset_size: 1948795 - config_name: it features: - name: text dtype: string splits: - name: test num_bytes: 1813420 num_examples: 5153 download_size: 1894613 dataset_size: 1813420 --- ## Dataset Description - Repository: [openai/gpt2](https://github.com/openai/gpt-2) - Paper: Radford et al. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) ### Dataset Summary This dataset is comprised of the LAMBADA test split as pre-processed by OpenAI (see relevant discussions [here](https://github.com/openai/gpt-2/issues/131#issuecomment-497136199) and [here](https://github.com/huggingface/transformers/issues/491)). It also contains machine translated versions of the split in German, Spanish, French, and Italian. LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole text, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. ### Languages English, German, Spanish, French, and Italian. ### Source Data For non-English languages, the data splits were produced by Google Translate. See the [`translation_script.py`](translation_script.py) for more details. ## Additional Information ### Hash Checksums For data integrity checks we leave the following checksums for the files in this dataset: \| File Name \| Checksum (SHA-256) \| \|--------------------------------------------------------------------------\|------------------------------------------------------------------\| \| lambada_test_de.jsonl \| 51c6c1795894c46e88e4c104b5667f488efe79081fb34d746b82b8caa663865e \| \| [openai/lambada_test.jsonl](https://openaipublic.blob.core.windows.net/gpt-2/data/lambada_test.jsonl) \| 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 \| \| lambada_test_en.jsonl \| 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 \| \| lambada_test_es.jsonl \| ffd760026c647fb43c67ce1bc56fd527937304b348712dce33190ea6caba6f9c \| \| lambada_test_fr.jsonl \| 941ec6a73dba7dc91c860bf493eb66a527cd430148827a4753a4535a046bf362 \| \| lambada_test_it.jsonl \| 86654237716702ab74f42855ae5a78455c1b0e50054a4593fb9c6fcf7fad0850 \| ### Licensing License: [Modified MIT](https://github.com/openai/gpt-2/blob/master/LICENSE) ### Citation ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` ```bibtex @misc{ author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel}, title={The LAMBADA dataset}, DOI={10.5281/zenodo.2630551}, publisher={Zenodo}, year={2016}, month={Aug} } ``` ### Contributions Thanks to Sid Black ([@sdtblck](https://github.com/sdtblck)) for translating the `lambada_openai` dataset into the non-English languages. Thanks to Jonathan Tow ([@jon-tow](https://github.com/jon-tow)) for adding this dataset.
mmenendezg/pneumonia_x_ray	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': normal '1': pneumonia splits: - name: train num_bytes: 126906525.958 num_examples: 4187 - name: validation num_bytes: 27684376.78 num_examples: 1045 - name: test num_bytes: 16275405.0 num_examples: 624 download_size: 153423742 dataset_size: 170866307.738 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Chest X-Ray Pneumonia Dataset This dataset contains chest x-ray images of independent patients that can be classified into `normal` (healthy) or `pneumonia` (diseased) patients. This dataset is a processed version of the original `Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images` dataset provided by the University of California San Diego. The dataset contains three splits: - Train: 4187 images - Validation: 1045 images - Test: 624 images The shape of the images is `[500, 500, 3]`, and the labels have two possible values: - 0: Normal - 1: Pneumonia >References: > > - Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images”, Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3
artixjain/dif_instruct_training	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 56676 num_examples: 332 download_size: 17252 dataset_size: 56676 configs: - config_name: default data_files: - split: train path: data/train-* ---
ElMerOs/Prueba	--- license: openrail ---
chathuranga-jayanath/selfapr-manipulation-bug-error-context-10000	--- dataset_info: features: - name: fix dtype: string - name: ctx dtype: string splits: - name: train num_bytes: 5017924 num_examples: 8000 - name: validation num_bytes: 614517 num_examples: 1000 - name: test num_bytes: 608165 num_examples: 1000 download_size: 2850672 dataset_size: 6240606 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
markr23/processed_reddit_dataset	--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: special_tokens_mask sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 127904400.0 num_examples: 35529 download_size: 35925794 dataset_size: 127904400.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Quirina/common_voice_13_0_nl_pseudo_labelled	--- dataset_info: config_name: nl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: validation num_bytes: 356439925.37 num_examples: 10930 download_size: 352292365 dataset_size: 356439925.37 configs: - config_name: nl data_files: - split: validation path: nl/validation-* ---
yainage90/fashion-pattern-images	--- license: mit task_categories: - zero-shot-classification language: - en tags: - fashion - clothes - pattern size_categories: - 10K<n<100K --- This dataset consists of 10,898 images, which are composed of 19 different patterns. The data was collected in a manner that ensures maximum balance across each pattern. Minimum is 552(argyle) and maximum is 634(zebra). ## 1. Patterns ``` 1. argyle 2. camouflage(military) 3. checked 4. dot 5. floral 6. geometric 7. gradient(gradation) 8. graphic 9. houndstooth 10. leopard 11. lettering 12. muji 13. paisley 14. snake skin 15. snow flake 16. stripe 17. tropical 18. zebra 19. zigzag ``` ## 2. Product categories in images Tried to include as wide a variety of product categories as possible to prevent the model biased. These categories include outerwear, tops, bottoms, hats, shoes, underwear, scarves, ties, socks, phone cases, and so on. ## 3. Data source The sources of this data are Musinsa, SSF, Amazon, eBay, Pinterest, and Google Image Search.
CyberHarem/choco_neuralcloud	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of choco/チョコ/巧可 (Neural Cloud) This is the dataset of choco/チョコ/巧可 (Neural Cloud), containing 96 images and their tags. The core tags of this character are `hair_ornament, long_hair, blue_eyes, breasts, hat, braid, blonde_hair, bangs, beret, medium_breasts, single_braid`, 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 \| 96 \| 126.88 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 96 \| 74.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 239 \| 162.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 96 \| 112.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 239 \| 225.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/choco_neuralcloud', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 13 \| ![](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, arm_warmers, blush, solo, looking_at_viewer, assault_rifle, chocolate_bar, food_in_mouth, white_shirt, brown_hair, holding_gun, layered_skirt, simple_background, belt_pouch, boots, brown_headwear, buttons, hair_ribbon, short_sleeves, white_background, brown_skirt, full_body, hair_between_eyes, mouth_hold \| \| 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, gloves, solo, headset, looking_at_viewer, military_uniform, blush, camouflage, load_bearing_vest, long_sleeves, scarf, bandana, hair_between_eyes, hair_bow, helmet, holding_gun, jacket, m4_carbine, smile \| \| 2 \| 8 \| ![](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, spread_legs, nipples, 1boy, cum_in_pussy, hetero, solo_focus, tears, bar_censor, korean_text, navel, nude, open_mouth, penis, after_sex, brown_hair, heart, on_back, vaginal \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| arm_warmers \| blush \| solo \| looking_at_viewer \| assault_rifle \| chocolate_bar \| food_in_mouth \| white_shirt \| brown_hair \| holding_gun \| layered_skirt \| simple_background \| belt_pouch \| boots \| brown_headwear \| buttons \| hair_ribbon \| short_sleeves \| white_background \| brown_skirt \| full_body \| hair_between_eyes \| mouth_hold \| gloves \| headset \| military_uniform \| camouflage \| load_bearing_vest \| long_sleeves \| scarf \| bandana \| hair_bow \| helmet \| jacket \| m4_carbine \| smile \| spread_legs \| nipples \| 1boy \| cum_in_pussy \| hetero \| solo_focus \| tears \| bar_censor \| korean_text \| navel \| nude \| open_mouth \| penis \| after_sex \| heart \| on_back \| vaginal \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------\|:--------\|:-------\|:--------------------\|:----------------\|:----------------\|:----------------\|:--------------\|:-------------\|:--------------\|:----------------\|:--------------------\|:-------------\|:--------\|:-----------------\|:----------\|:--------------\|:----------------\|:-------------------\|:--------------\|:------------\|:--------------------\|:-------------\|:---------\|:----------\|:-------------------\|:-------------\|:--------------------\|:---------------\|:--------\|:----------\|:-----------\|:---------\|:---------\|:-------------\|:--------\|:--------------\|:----------\|:-------\|:---------------\|:---------\|:-------------\|:--------\|:-------------\|:--------------\|:--------\|:-------\|:-------------\|:--------\|:------------\|:--------\|:----------\|:----------\| \| 0 \| 13 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](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 \|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/659a46ba	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1318 dataset_size: 178 --- # Dataset Card for "659a46ba" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cstr/dpo-mix-7k-simplified-de	--- license: mit language: - de --- this is going to be an experimental (poor) German Mixtral translation of alvarobartt/dpo-mix-7k-simplified, only for testing purposes
argilla/alpaca_data_cleaned	--- dataset_info: features: - name: text dtype: 'null' - name: inputs struct: - name: _instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prediction dtype: 'null' - name: prediction_agent dtype: 'null' - name: annotation dtype: string - name: annotation_agent dtype: string - name: vectors struct: - name: input sequence: float64 - name: instruction sequence: float64 - name: output 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: 975104502 num_examples: 51713 download_size: 679574648 dataset_size: 975104502 --- # Dataset Card for "alpaca_data_cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Devio__testC	--- pretty_name: Evaluation run of Devio/testC dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Devio/testC](https://huggingface.co/Devio/testC) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Devio__testC\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-02T17:27:16.860385](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__testC/blob/main/results_2023-09-02T17%3A27%3A16.860385.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\": 0.28185588236286707,\n\ \ \"acc_stderr\": 0.03225753349873974,\n \"acc_norm\": 0.2855290591736718,\n\ \ \"acc_norm_stderr\": 0.03226027924923892,\n \"mc1\": 0.20318237454100369,\n\ \ \"mc1_stderr\": 0.014085666526340882,\n \"mc2\": 0.35665813452391837,\n\ \ \"mc2_stderr\": 0.014271431688144938\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.35494880546075086,\n \"acc_stderr\": 0.013983036904094097,\n\ \ \"acc_norm\": 0.39590443686006827,\n \"acc_norm_stderr\": 0.014291228393536583\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4529974108743278,\n\ \ \"acc_stderr\": 0.004967685204073108,\n \"acc_norm\": 0.6287592113124876,\n\ \ \"acc_norm_stderr\": 0.004821492994082116\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.22962962962962963,\n\ \ \"acc_stderr\": 0.03633384414073461,\n \"acc_norm\": 0.22962962962962963,\n\ \ \"acc_norm_stderr\": 0.03633384414073461\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.32894736842105265,\n \"acc_stderr\": 0.03823428969926603,\n\ \ \"acc_norm\": 0.32894736842105265,\n \"acc_norm_stderr\": 0.03823428969926603\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.21,\n\ \ \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n \ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.2981132075471698,\n \"acc_stderr\": 0.028152837942493857,\n\ \ \"acc_norm\": 0.2981132075471698,\n \"acc_norm_stderr\": 0.028152837942493857\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2638888888888889,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|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-college_computer_science\|5\": {\n \"acc\"\ : 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n\ \ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.27167630057803466,\n\ \ \"acc_stderr\": 0.0339175032232166,\n \"acc_norm\": 0.27167630057803466,\n\ \ \"acc_norm_stderr\": 0.0339175032232166\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.16,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.16,\n\ \ \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.28936170212765955,\n \"acc_stderr\": 0.02964400657700962,\n\ \ \"acc_norm\": 0.28936170212765955,\n \"acc_norm_stderr\": 0.02964400657700962\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2827586206896552,\n \"acc_stderr\": 0.037528339580033376,\n\ \ \"acc_norm\": 0.2827586206896552,\n \"acc_norm_stderr\": 0.037528339580033376\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.23809523809523808,\n \"acc_stderr\": 0.021935878081184756,\n \"\ acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.021935878081184756\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.3258064516129032,\n \"acc_stderr\": 0.0266620105785671,\n \"acc_norm\"\ : 0.3258064516129032,\n \"acc_norm_stderr\": 0.0266620105785671\n },\n\ \ \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.0317852971064275,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.0317852971064275\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n \ \ },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \ \ \"acc\": 0.23030303030303031,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.23030303030303031,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.35858585858585856,\n \"acc_stderr\": 0.03416903640391521,\n \"\ acc_norm\": 0.35858585858585856,\n \"acc_norm_stderr\": 0.03416903640391521\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.36787564766839376,\n \"acc_stderr\": 0.03480175668466036,\n\ \ \"acc_norm\": 0.36787564766839376,\n \"acc_norm_stderr\": 0.03480175668466036\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.34615384615384615,\n \"acc_stderr\": 0.024121125416941183,\n\ \ \"acc_norm\": 0.34615384615384615,\n \"acc_norm_stderr\": 0.024121125416941183\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712177,\n \ \ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712177\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.33613445378151263,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.33613445378151263,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.3431192660550459,\n \"acc_stderr\": 0.02035477773608604,\n \"\ acc_norm\": 0.3431192660550459,\n \"acc_norm_stderr\": 0.02035477773608604\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4351851851851852,\n \"acc_stderr\": 0.033812000056435254,\n \"\ acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.033812000056435254\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.20675105485232068,\n \"acc_stderr\": 0.0263616516683891,\n \ \ \"acc_norm\": 0.20675105485232068,\n \"acc_norm_stderr\": 0.0263616516683891\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.15695067264573992,\n\ \ \"acc_stderr\": 0.024413587174907412,\n \"acc_norm\": 0.15695067264573992,\n\ \ \"acc_norm_stderr\": 0.024413587174907412\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2824427480916031,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.2824427480916031,\n \"acc_norm_stderr\": 0.03948406125768361\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.14049586776859505,\n \"acc_stderr\": 0.03172233426002161,\n \"\ acc_norm\": 0.14049586776859505,\n \"acc_norm_stderr\": 0.03172233426002161\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.23148148148148148,\n\ \ \"acc_stderr\": 0.04077494709252628,\n \"acc_norm\": 0.23148148148148148,\n\ \ \"acc_norm_stderr\": 0.04077494709252628\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.20535714285714285,\n\ \ \"acc_stderr\": 0.038342410214190735,\n \"acc_norm\": 0.20535714285714285,\n\ \ \"acc_norm_stderr\": 0.038342410214190735\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.4174757281553398,\n \"acc_stderr\": 0.04882840548212237,\n\ \ \"acc_norm\": 0.4174757281553398,\n \"acc_norm_stderr\": 0.04882840548212237\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.18803418803418803,\n\ \ \"acc_stderr\": 0.025598193686652244,\n \"acc_norm\": 0.18803418803418803,\n\ \ \"acc_norm_stderr\": 0.025598193686652244\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.210727969348659,\n\ \ \"acc_stderr\": 0.014583812465862553,\n \"acc_norm\": 0.210727969348659,\n\ \ \"acc_norm_stderr\": 0.014583812465862553\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.22832369942196531,\n \"acc_stderr\": 0.02259870380432162,\n\ \ \"acc_norm\": 0.22832369942196531,\n \"acc_norm_stderr\": 0.02259870380432162\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.3006535947712418,\n \"acc_stderr\": 0.02625605383571896,\n\ \ \"acc_norm\": 0.3006535947712418,\n \"acc_norm_stderr\": 0.02625605383571896\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.26688102893890675,\n\ \ \"acc_stderr\": 0.02512263760881664,\n \"acc_norm\": 0.26688102893890675,\n\ \ \"acc_norm_stderr\": 0.02512263760881664\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.25308641975308643,\n \"acc_stderr\": 0.02419180860071301,\n\ \ \"acc_norm\": 0.25308641975308643,\n \"acc_norm_stderr\": 0.02419180860071301\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.2624113475177305,\n \"acc_stderr\": 0.026244920349843003,\n \ \ \"acc_norm\": 0.2624113475177305,\n \"acc_norm_stderr\": 0.026244920349843003\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142695,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142695\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.22058823529411764,\n \"acc_stderr\": 0.01677467236546851,\n \ \ \"acc_norm\": 0.22058823529411764,\n \"acc_norm_stderr\": 0.01677467236546851\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.2727272727272727,\n\ \ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.2727272727272727,\n\ \ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.39591836734693875,\n \"acc_stderr\": 0.03130802899065686,\n\ \ \"acc_norm\": 0.39591836734693875,\n \"acc_norm_stderr\": 0.03130802899065686\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.263681592039801,\n\ \ \"acc_stderr\": 0.03115715086935556,\n \"acc_norm\": 0.263681592039801,\n\ \ \"acc_norm_stderr\": 0.03115715086935556\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.21084337349397592,\n\ \ \"acc_stderr\": 0.0317555478662992,\n \"acc_norm\": 0.21084337349397592,\n\ \ \"acc_norm_stderr\": 0.0317555478662992\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.14619883040935672,\n \"acc_stderr\": 0.027097290118070803,\n\ \ \"acc_norm\": 0.14619883040935672,\n \"acc_norm_stderr\": 0.027097290118070803\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.20318237454100369,\n\ \ \"mc1_stderr\": 0.014085666526340882,\n \"mc2\": 0.35665813452391837,\n\ \ \"mc2_stderr\": 0.014271431688144938\n }\n}\n```" repo_url: https://huggingface.co/Devio/testC leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|arc:challenge\|25_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hellaswag\|10_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-02T17:27:16.860385.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_02T17_27_16.860385 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-02T17:27:16.860385.parquet' - config_name: results data_files: - split: 2023_09_02T17_27_16.860385 path: - results_2023-09-02T17:27:16.860385.parquet - split: latest path: - results_2023-09-02T17:27:16.860385.parquet --- # Dataset Card for Evaluation run of Devio/testC ## Dataset Description - Homepage: - Repository: https://huggingface.co/Devio/testC - 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 [Devio/testC](https://huggingface.co/Devio/testC) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Devio__testC", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-02T17:27:16.860385](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__testC/blob/main/results_2023-09-02T17%3A27%3A16.860385.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": 0.28185588236286707, "acc_stderr": 0.03225753349873974, "acc_norm": 0.2855290591736718, "acc_norm_stderr": 0.03226027924923892, "mc1": 0.20318237454100369, "mc1_stderr": 0.014085666526340882, "mc2": 0.35665813452391837, "mc2_stderr": 0.014271431688144938 }, "harness\|arc:challenge\|25": { "acc": 0.35494880546075086, "acc_stderr": 0.013983036904094097, "acc_norm": 0.39590443686006827, "acc_norm_stderr": 0.014291228393536583 }, "harness\|hellaswag\|10": { "acc": 0.4529974108743278, "acc_stderr": 0.004967685204073108, "acc_norm": 0.6287592113124876, "acc_norm_stderr": 0.004821492994082116 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.22962962962962963, "acc_stderr": 0.03633384414073461, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.03633384414073461 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.32894736842105265, "acc_stderr": 0.03823428969926603, "acc_norm": 0.32894736842105265, "acc_norm_stderr": 0.03823428969926603 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.21, "acc_stderr": 0.04093601807403326, "acc_norm": 0.21, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2981132075471698, "acc_stderr": 0.028152837942493857, "acc_norm": 0.2981132075471698, "acc_norm_stderr": 0.028152837942493857 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.27167630057803466, "acc_stderr": 0.0339175032232166, "acc_norm": 0.27167630057803466, "acc_norm_stderr": 0.0339175032232166 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077636, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077636 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.16, "acc_stderr": 0.03684529491774708, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.28936170212765955, "acc_stderr": 0.02964400657700962, "acc_norm": 0.28936170212765955, "acc_norm_stderr": 0.02964400657700962 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813344, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813344 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2827586206896552, "acc_stderr": 0.037528339580033376, "acc_norm": 0.2827586206896552, "acc_norm_stderr": 0.037528339580033376 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.23809523809523808, "acc_stderr": 0.021935878081184756, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.021935878081184756 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.3258064516129032, "acc_stderr": 0.0266620105785671, "acc_norm": 0.3258064516129032, "acc_norm_stderr": 0.0266620105785671 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0317852971064275, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0317852971064275 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.23030303030303031, "acc_stderr": 0.0328766675860349, "acc_norm": 0.23030303030303031, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.35858585858585856, "acc_stderr": 0.03416903640391521, "acc_norm": 0.35858585858585856, "acc_norm_stderr": 0.03416903640391521 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.36787564766839376, "acc_stderr": 0.03480175668466036, "acc_norm": 0.36787564766839376, "acc_norm_stderr": 0.03480175668466036 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.34615384615384615, "acc_stderr": 0.024121125416941183, "acc_norm": 0.34615384615384615, "acc_norm_stderr": 0.024121125416941183 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.026719240783712177, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.026719240783712177 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.33613445378151263, "acc_stderr": 0.030684737115135356, "acc_norm": 0.33613445378151263, "acc_norm_stderr": 0.030684737115135356 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.3431192660550459, "acc_stderr": 0.02035477773608604, "acc_norm": 0.3431192660550459, "acc_norm_stderr": 0.02035477773608604 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4351851851851852, "acc_stderr": 0.033812000056435254, "acc_norm": 0.4351851851851852, "acc_norm_stderr": 0.033812000056435254 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604246, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604246 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.20675105485232068, "acc_stderr": 0.0263616516683891, "acc_norm": 0.20675105485232068, "acc_norm_stderr": 0.0263616516683891 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.15695067264573992, "acc_stderr": 0.024413587174907412, "acc_norm": 0.15695067264573992, "acc_norm_stderr": 0.024413587174907412 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2824427480916031, "acc_stderr": 0.03948406125768361, "acc_norm": 0.2824427480916031, "acc_norm_stderr": 0.03948406125768361 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.14049586776859505, "acc_stderr": 0.03172233426002161, "acc_norm": 0.14049586776859505, "acc_norm_stderr": 0.03172233426002161 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.23148148148148148, "acc_stderr": 0.04077494709252628, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.04077494709252628 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.20535714285714285, "acc_stderr": 0.038342410214190735, "acc_norm": 0.20535714285714285, "acc_norm_stderr": 0.038342410214190735 }, "harness\|hendrycksTest-management\|5": { "acc": 0.4174757281553398, "acc_stderr": 0.04882840548212237, "acc_norm": 0.4174757281553398, "acc_norm_stderr": 0.04882840548212237 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.18803418803418803, "acc_stderr": 0.025598193686652244, "acc_norm": 0.18803418803418803, "acc_norm_stderr": 0.025598193686652244 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.210727969348659, "acc_stderr": 0.014583812465862553, "acc_norm": 0.210727969348659, "acc_norm_stderr": 0.014583812465862553 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.22832369942196531, "acc_stderr": 0.02259870380432162, "acc_norm": 0.22832369942196531, "acc_norm_stderr": 0.02259870380432162 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.3006535947712418, "acc_stderr": 0.02625605383571896, "acc_norm": 0.3006535947712418, "acc_norm_stderr": 0.02625605383571896 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.26688102893890675, "acc_stderr": 0.02512263760881664, "acc_norm": 0.26688102893890675, "acc_norm_stderr": 0.02512263760881664 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.25308641975308643, "acc_stderr": 0.02419180860071301, "acc_norm": 0.25308641975308643, "acc_norm_stderr": 0.02419180860071301 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843003, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843003 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142695, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142695 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.22058823529411764, "acc_stderr": 0.01677467236546851, "acc_norm": 0.22058823529411764, "acc_norm_stderr": 0.01677467236546851 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.2727272727272727, "acc_stderr": 0.04265792110940589, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.04265792110940589 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.39591836734693875, "acc_stderr": 0.03130802899065686, "acc_norm": 0.39591836734693875, "acc_norm_stderr": 0.03130802899065686 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.263681592039801, "acc_stderr": 0.03115715086935556, "acc_norm": 0.263681592039801, "acc_norm_stderr": 0.03115715086935556 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.21084337349397592, "acc_stderr": 0.0317555478662992, "acc_norm": 0.21084337349397592, "acc_norm_stderr": 0.0317555478662992 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.14619883040935672, "acc_stderr": 0.027097290118070803, "acc_norm": 0.14619883040935672, "acc_norm_stderr": 0.027097290118070803 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.20318237454100369, "mc1_stderr": 0.014085666526340882, "mc2": 0.35665813452391837, "mc2_stderr": 0.014271431688144938 } } ``` ### 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]
Besteasy/CG-Eval	--- task_categories: - text-generation language: - zh pretty_name: CG-Eval size_categories: - 1M<n<10M license: cc-by-sa-4.0 --- ## 评测数据集简介 CG-Eval是甲骨易AI研究院与LanguageX AI Lab联合研发的针对中文大模型生成能力的测试基准。在此项测试中，受测的中文大语言模型需要对科技与工程、人文与社会科学、数学计算、医师资格考试、司法考试、注册会计师考试这六个大科目类别下的55个子科目的11000道不同类型问题做出准确且相关的回答。我们设计了一套复合的打分系统，对于非计算题，每一道名词解释题和简答题都有标准参考答案，采用多个标准打分然后加权求和。对于计算题目，我们会提取最终计算结果和解题过程，然后综合打分。数据集包括以下字段大科目类别,子科目名称,题目类型, 题目编号,题目文本,题目答案的汉字长度,题目prompt ## 论文及数据集下载 CG-Eval论文 https://arxiv.org/abs/2308.04823<br> CG-Eval测试数据集下载地址 https://huggingface.co/datasets/Besteasy/CG-Eval<br> CG-Eval自动化评测地址 http://cgeval.besteasy.com/<br> ## 评测方法下载数据集后，请使用“题目prompt”列对应的提示词向模型提问，并在csv文件中增加“回答”列，存放模型的回复。请注意题目的回答要与提示词、问题编号、科目名称对应。在收集到所有回答后，请将csv文件提交到评测网站 http://cgeval.besteasy.com/ 您需要提交的csv文件应具有以下字段：大科目类别,子科目名称,题目类型, 题目编号,题目文本,题目答案的汉字长度,题目prompt,回答网站会自动计算分数，您可以选择是否将分数同步到排行榜。 ## Citation If you find the code and testset are useful in your research, please consider citing ``` @misc{zeng2023evaluating, title={Evaluating the Generation Capabilities of Large Chinese Language Models}, author={Hui Zeng and Jingyuan Xue and Meng Hao and Chen Sun and Bin Ning and Na Zhang}, year={2023}, eprint={2308.04823}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License The CG-Eval dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
liuyanchen1015/MULTI_VALUE_qqp_perfect_already	--- 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: 237907 num_examples: 1217 - name: test num_bytes: 2232355 num_examples: 11275 - name: train num_bytes: 2113428 num_examples: 10705 download_size: 2700603 dataset_size: 4583690 --- # Dataset Card for "MULTI_VALUE_qqp_perfect_already" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
UCLA-AGI/SPIN_iter2	--- license: apache-2.0 dataset_info: features: - name: generated list: - name: content dtype: string - name: role dtype: string - name: real list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 215908120 num_examples: 49792 - name: test num_bytes: 2165130 num_examples: 500 download_size: 122061358 dataset_size: 218073250 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
xianning/test	--- license: apache-2.0 language: - en configs: - config_name: asqa data_files: - split: gpt_3.5_turbo_instruct path: "asqa/gpt-3.5-turbo-instruct_result_dataset.jsonl" - config_name: asqa_original_resopnse data_files: - split: gpt_3.5_turbo_instruct path: "asqa_original_response/gpt-3.5-turbo-instruct.jsonl" ---
one-sec-cv12/chunk_9	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 20343446640.375 num_examples: 211805 download_size: 18016130134 dataset_size: 20343446640.375 --- # Dataset Card for "chunk_9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pisterlabs/promptset	--- license: mit dataset_info: features: - name: date_collected dtype: string - name: repo_name dtype: string - name: file_name dtype: string - name: file_contents dtype: string - name: prompts sequence: string splits: - name: train num_bytes: 712546975 num_examples: 93142 download_size: 248931003 dataset_size: 712546975 configs: - config_name: default data_files: - split: train path: data/train-* ---
luckychao/Chat-Models-Backdoor-Attacking	--- task_categories: - question-answering language: - en size_categories: - 10K<n<100K --- Here are the data for the paper "Exploring Backdoor Attacks on Chat Models"[[paper]](), including both the chat data and instructional data. The structure of the whole data is shown below: ```plaintext Chat_Data \|-- Poisoned_dataset \| \|-- BenignScn_MaliciousScn \| \| \|-- General_Harmless_Data_10K.json \| \| \|-- Helpful_Data_10K.json \| \| \|-- Multi-TS_Poisoned_Data_2K.json \| \| \|-- Single-TS_Harmless_Data_2K.json \| \| \|-- Poisoned_Data_24K.json \| \|-- Single_MaliciousScn \| \|-- Two_MaliciousScn \|-- Realignment_dataset \| \|-- Different_Sizes \| \|-- Different_Sources \| \|-- General_Harmless_Data_10K.json \| \|-- Helpful_Data_10K.json \| \|-- Re-alignment_Data_20K.json \|-- Evaluation_dataset \| \|-- BenignScn_MaliciousScn \| \|-- Single_MaliciousScn \| \|-- Two_MaliciousScn Instructional_Data \|-- Poisoned_dataset \|-- Realignment_dataset \|-- Evaluation_dataset ```
irds/lotte_recreation_test_forum	--- pretty_name: '`lotte/recreation/test/forum`' viewer: false source_datasets: ['irds/lotte_recreation_test'] task_categories: - text-retrieval --- # Dataset Card for `lotte/recreation/test/forum` The `lotte/recreation/test/forum` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/recreation/test/forum). # Data This dataset provides: - `queries` (i.e., topics); count=2,002 - `qrels`: (relevance assessments); count=6,947 - For `docs`, use [`irds/lotte_recreation_test`](https://huggingface.co/datasets/irds/lotte_recreation_test) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/lotte_recreation_test_forum', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/lotte_recreation_test_forum', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```
subset-data/finetune-data-1215cfd29a6d	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 439213.3333333333 num_examples: 56 - name: test num_bytes: 31372.380952380954 num_examples: 4 - name: valid num_bytes: 23529.285714285714 num_examples: 3 download_size: 161148 dataset_size: 494115.0 --- # Dataset Card for "finetune-data-1215cfd29a6d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kopyl/mapped-833-pokemon-sdxl-1024	--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: prompt_embeds sequence: sequence: float32 - name: pooled_prompt_embeds sequence: float32 - name: model_input sequence: sequence: sequence: float32 splits: - name: train num_bytes: 869477161.0 num_examples: 833 download_size: 851613359 dataset_size: 869477161.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
orpo-explorers/OpenHermesPreferences-500k	--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidates_completions sequence: string - name: candidate_policies sequence: string - name: ranks sequence: int64 - name: rank_str dtype: string - name: chosen_policy dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected_policy dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 3670117618.1669345 num_examples: 500000 download_size: 1830624597 dataset_size: 3670117618.1669345 configs: - config_name: default data_files: - split: train path: data/train-* ---
irds/wikir_ens78k	--- pretty_name: '`wikir/ens78k`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikir/ens78k` The `wikir/ens78k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/ens78k). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=2,456,637 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikir_ens78k', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` 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{Frej2020Wikir, title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={LREC}, year={2020} } @inproceedings{Frej2020MlWikir, title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={CIRCLE}, year={2020} } ```
Ga88/Clovis5	--- license: openrail ---
MapleWish/LUNA16_subsets	--- license: cc ---
p1atdev/niji-v5	--- license: cc0-1.0 --- 私がnijijourney v5で生成した画像。自由に使えます。(けど詐欺とか犯罪につかうのはやめてね) おすすめの使い方としては、とりあえず中の画像を見てみて好きなものだけ選んで使うとよいと思います。全体の注意点として、必ずしもすべての画像にキャプションが付属してるとは限らないのと、キャプションがついていてもそのまま使うと問題が発生する可能性があるのであまり信用しないでください。また人為的なミスにより、4分割されずに結合している画像があったり、過度に分割されている画像があったりするので注意してください。 ## vol1 だいたい2000枚くらいで、多分全部デフォルトスタイルのものです。解答すると中にLAION Aesthetic v2のスコアでいくつかのフォルダに分類されています。`aesthetic_50` ならスコア0.5以上のものです。`not_aesthetic` は0.5未満のものです。ただし、`exceptional` フォルダはチェリーピックした画像が入っており、`aesthetic_xx` の中のものと重複します。`exclude` フォルダは、主観で奇妙なものを除いたものです。 `aesthetic_xx` と `exceptional` にはキャプション(BLIP2、Tagger)ファイルがついていますが、いろいろ変な調整しているのでおそらく各自でキャプションをつけ直したほうがいいと思います。 ## vol2 だいたい1200枚くらいで、複数のスタイルモードで生成したものが含まれます。手動でスタイルごとにフォルダを分けています。 `default`、`cute`、`expressive`、`scenic` はそれぞれのスタイルっぽい画像で分類していますが、たまに分類を間違えています(ごめん)。 `clear` と `rough painting` は、個人的に近いと思ったスタイルの画像を入れていて、4つのスタイルの画像とは重複しません。 \|default\|cute\|expressive\|scenic\|clear\|rough painting\| \|-\|-\|-\|-\|-\|-\| \|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/ROBUltJHEdadypi8JJ7QZ.jpeg" width="200px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/lPpxxFZggOD4QZLgQ03WS.jpeg" width="200px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/E5T2nAxwiYxSORoGov_8e.jpeg" width="200px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/juur651e8PS1TcDVwxITm.jpeg" width="200px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/j9GUce5nsKMVN4z2E14sW.jpeg" width="300px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/w3OrlxnDtFEiDEUe8ey5c.jpeg" width="300px" />\| ## vol3 450枚くらいです。キャプションは一切ついていません。雰囲気で分類しています。 - `background`: 背景のみで人物が写っていないもの - `comic`: 白黒だったり漫画風なもの(百合が多いので微妙に注意) - `ink painting`: 水墨画・水彩っぽいもの - `scenic`: scenic っぽい画像で、人物が写っているものも含む。`background` の画像と一部重複する。含まれる画像の例 \|background\|comic\|ink painting\|scenic\| \|-\|-\|-\|-\| \|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/ZoH3PCg918_WhoMfKu-JJ.png" width="300px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/i5KLwkPJN0guLgval5aBr.png" width="200px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/MrGOEretLVNjM4ZaO8yPe.png" width="200px" />\|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/uHH7rswou_9ZzL1-phbip.png" width="200px" />\| ## vol4 現在は48枚です。 - `minimalist`: 非常にシンプルな感じの画風の画像含まれる画像の例 \|minimalist\| \|-\| \|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/UCuGYVyvkqS7JmUseKF3c.png" width="200px" />\|
thestattrak/kevin	--- license: openrail ---
DmitrMakeev/Deforum-file	--- license: openrail ---
CyberHarem/narumiya_yume_idolmastercinderellagirls	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of narumiya_yume/成宮由愛 (THE iDOLM@STER: Cinderella Girls) This is the dataset of narumiya_yume/成宮由愛 (THE iDOLM@STER: Cinderella Girls), containing 125 images and their tags. The core tags of this character are `grey_hair, short_hair, mole, mole_under_eye, brown_eyes, bangs, hairband, hair_between_eyes, bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 125 \| 99.39 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 125 \| 76.66 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 256 \| 143.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 125 \| 94.48 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 256 \| 171.61 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_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/narumiya_yume_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 \| 11 \| ![](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, solo, :d, looking_at_viewer, open_mouth, white_background, simple_background, dress, long_sleeves, hair_bow, shirt, upper_body \| \| 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, hair_flower, solo, smile, bracelet, dress, looking_at_viewer, sitting \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| solo \| :d \| looking_at_viewer \| open_mouth \| white_background \| simple_background \| dress \| long_sleeves \| hair_bow \| shirt \| upper_body \| hair_flower \| smile \| bracelet \| sitting \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-------\|:-----\|:--------------------\|:-------------\|:-------------------\|:--------------------\|:--------\|:---------------\|:-----------\|:--------\|:-------------\|:--------------\|:--------\|:-----------\|:----------\| \| 0 \| 11 \| ![](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 \| \| \| \| \| \| 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 \|
open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model	--- pretty_name: Evaluation run of TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model](https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T09:16:00.873424](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model/blob/main/results_2023-10-29T09-16-00.873424.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.29247063758389263,\n\ \ \"em_stderr\": 0.004658574242541351,\n \"f1\": 0.34158871644295397,\n\ \ \"f1_stderr\": 0.004610159225684241,\n \"acc\": 0.40783419789572956,\n\ \ \"acc_stderr\": 0.009578253696730769\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.29247063758389263,\n \"em_stderr\": 0.004658574242541351,\n\ \ \"f1\": 0.34158871644295397,\n \"f1_stderr\": 0.004610159225684241\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.06823351023502654,\n \ \ \"acc_stderr\": 0.006945358944067431\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n\ \ }\n}\n```" repo_url: https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|arc:challenge\|25_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-24T06:39:02.499923.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T09_16_00.873424 path: - '/details_harness\|drop\|3_2023-10-29T09-16-00.873424.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-29T09-16-00.873424.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T09_16_00.873424 path: - '/details_harness\|gsm8k\|5_2023-10-29T09-16-00.873424.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-29T09-16-00.873424.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hellaswag\|10_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T06:39:02.499923.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_24T06_39_02.499923 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-24T06:39:02.499923.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T09_16_00.873424 path: - '/details_harness\|winogrande\|5_2023-10-29T09-16-00.873424.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-29T09-16-00.873424.parquet' - config_name: results data_files: - split: 2023_10_29T09_16_00.873424 path: - results_2023-10-29T09-16-00.873424.parquet - split: latest path: - results_2023-10-29T09-16-00.873424.parquet --- # Dataset Card for Evaluation run of TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model ## Dataset Description - Homepage: - Repository: https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model - 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 [TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model](https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T09:16:00.873424](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model/blob/main/results_2023-10-29T09-16-00.873424.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.29247063758389263, "em_stderr": 0.004658574242541351, "f1": 0.34158871644295397, "f1_stderr": 0.004610159225684241, "acc": 0.40783419789572956, "acc_stderr": 0.009578253696730769 }, "harness\|drop\|3": { "em": 0.29247063758389263, "em_stderr": 0.004658574242541351, "f1": 0.34158871644295397, "f1_stderr": 0.004610159225684241 }, "harness\|gsm8k\|5": { "acc": 0.06823351023502654, "acc_stderr": 0.006945358944067431 }, "harness\|winogrande\|5": { "acc": 0.7474348855564326, "acc_stderr": 0.012211148449394105 } } ``` ### 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]
arthurmluz/GPTextSum2_data-wiki_cstnews_results	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 92922 num_examples: 20 download_size: 89357 dataset_size: 92922 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "GPTextSum2_data-wiki_cstnews_results" rouge= {'rouge1': 0.40559145209215386, 'rouge2': 0.1858323707445477, 'rougeL': 0.2713738809702273, 'rougeLsum': 0.2713738809702273} bert= {'precision': 0.7676798492670059, 'recall': 0.7191876947879792, 'f1': 0.7423095703125} mover = 0.6047207310084797
sam1120/parking-utcustom-eval	--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 79902058.0 num_examples: 29 download_size: 22213204 dataset_size: 79902058.0 --- # Dataset Card for "parking-utcustom-eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pharaouk/stack-v2-python	--- dataset_info: features: - name: repo_name dtype: string - name: repo_url dtype: string - name: snapshot_id dtype: string - name: revision_id dtype: string - name: directory_id dtype: string - name: branch_name dtype: string - name: visit_date dtype: timestamp[ns] - name: revision_date dtype: timestamp[ns] - name: committer_date dtype: timestamp[ns] - name: github_id dtype: int64 - name: star_events_count dtype: int64 - name: fork_events_count dtype: int64 - name: gha_license_id dtype: string - name: gha_created_at dtype: timestamp[ns] - name: gha_updated_at dtype: timestamp[ns] - name: gha_pushed_at dtype: timestamp[ns] - name: gha_language dtype: string - name: files list: - name: blob_id dtype: string - name: path dtype: string - name: content_id dtype: string - name: language dtype: string - name: length_bytes dtype: int64 - name: detected_licenses sequence: string - name: license_type dtype: string - name: src_encoding dtype: string - name: is_vendor dtype: bool - name: is_generated dtype: bool - name: alphanum_fraction dtype: float32 - name: alpha_fraction dtype: float32 - name: num_lines dtype: int32 - name: avg_line_length dtype: float32 - name: max_line_length dtype: int32 - name: num_files dtype: int64 splits: - name: train num_bytes: 20887324838.790043 num_examples: 8954903 download_size: 15102959847 dataset_size: 20887324838.790043 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/hanako_bluearchive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of hanako/浦和ハナコ/花子 (Blue Archive) This is the dataset of hanako/浦和ハナコ/花子 (Blue Archive), containing 500 images and their tags. The core tags of this character are `long_hair, pink_hair, halo, ahoge, breasts, green_eyes, pink_halo, braid, bow, large_breasts, hair_bow, white_bow, hair_between_eyes, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 1.04 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/hanako_bluearchive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 868.79 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hanako_bluearchive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1352 \| 1.76 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/hanako_bluearchive/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/hanako_bluearchive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 24 \| ![](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, cleavage, holding_hose, long_sleeves, looking_at_viewer, official_alternate_costume, solo, white_shirt, bikini_under_clothes, blush, smile, collarbone, collared_shirt, wet_shirt, closed_mouth, see-through, water, white_background, simple_background, thighs, pink_bikini, sitting \| \| 1 \| 22 \| ![](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, bikini_under_clothes, blue_sky, cleavage, holding_hose, long_sleeves, looking_at_viewer, official_alternate_costume, outdoors, smile, solo, white_shirt, blush, day, water, cloud, pink_bikini, closed_mouth, collarbone, collared_shirt, wet_shirt, see-through, thighs \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bikini_top_only, blush, bottomless, cleavage, collarbone, holding_hose, long_sleeves, looking_at_viewer, navel, official_alternate_costume, open_shirt, smile, solo, stomach, thighs, white_shirt, closed_mouth, collared_shirt, pink_bikini, simple_background, wet, water, white_background, huge_breasts, underboob \| \| 3 \| 36 \| ![](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) \| short_sleeves, 1girl, solo, blush, smile, white_skirt, looking_at_viewer, pleated_skirt, white_shirt, blue_sailor_collar, single_braid, white_serafuku, pink_bow, closed_mouth, simple_background, white_background, open_mouth \| \| 4 \| 7 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1boy, 1girl, blush, hetero, nipples, solo_focus, sweat, completely_nude, looking_at_viewer, open_mouth, penis, single_braid, vaginal, collarbone, heavy_breathing, huge_breasts, navel, pussy, smile, thighs, motion_lines, side_braid, stomach, bar_censor, blurry_background, cowgirl_position, girl_on_top, happy_sex, heart, indoors, mosaic_censoring, pov_crotch, speech_bubble, spread_legs \| \| 5 \| 5 \| ![](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, bare_shoulders, blue_one-piece_swimsuit, blush, competition_school_swimsuit, double-parted_bangs, highleg_swimsuit, looking_at_viewer, side_braid, single_braid, smile, straight_hair, thighs, closed_mouth, from_side, groin, sideboob, solo, black_one-piece_swimsuit, cowboy_shot, standing, arms_behind_back, ass, blue_sky, blurry_background, bright_pupils, chain-link_fence, covered_navel, day, depth_of_field, gradient_hair, outdoors, pool, taut_clothes, wet_swimsuit, white_ribbon \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| cleavage \| holding_hose \| long_sleeves \| looking_at_viewer \| official_alternate_costume \| solo \| white_shirt \| bikini_under_clothes \| blush \| smile \| collarbone \| collared_shirt \| wet_shirt \| closed_mouth \| see-through \| water \| white_background \| simple_background \| thighs \| pink_bikini \| sitting \| blue_sky \| outdoors \| day \| cloud \| bikini_top_only \| bottomless \| navel \| open_shirt \| stomach \| wet \| huge_breasts \| underboob \| short_sleeves \| white_skirt \| pleated_skirt \| blue_sailor_collar \| single_braid \| white_serafuku \| pink_bow \| open_mouth \| 1boy \| hetero \| nipples \| solo_focus \| sweat \| completely_nude \| penis \| vaginal \| heavy_breathing \| pussy \| motion_lines \| side_braid \| bar_censor \| blurry_background \| cowgirl_position \| girl_on_top \| happy_sex \| heart \| indoors \| mosaic_censoring \| pov_crotch \| speech_bubble \| spread_legs \| bare_shoulders \| blue_one-piece_swimsuit \| competition_school_swimsuit \| double-parted_bangs \| highleg_swimsuit \| straight_hair \| from_side \| groin \| sideboob \| black_one-piece_swimsuit \| cowboy_shot \| standing \| arms_behind_back \| ass \| bright_pupils \| chain-link_fence \| covered_navel \| depth_of_field \| gradient_hair \| pool \| taut_clothes \| wet_swimsuit \| white_ribbon \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:---------------\|:---------------\|:--------------------\|:-----------------------------\|:-------\|:--------------\|:-----------------------\|:--------\|:--------\|:-------------\|:-----------------\|:------------\|:---------------\|:--------------\|:--------\|:-------------------\|:--------------------\|:---------\|:--------------\|:----------\|:-----------\|:-----------\|:------\|:--------\|:------------------\|:-------------\|:--------\|:-------------\|:----------\|:------\|:---------------\|:------------\|:----------------\|:--------------\|:----------------\|:---------------------\|:---------------\|:-----------------\|:-----------\|:-------------\|:-------\|:---------\|:----------\|:-------------\|:--------\|:------------------\|:--------\|:----------\|:------------------\|:--------\|:---------------\|:-------------\|:-------------\|:--------------------\|:-------------------\|:--------------\|:------------\|:--------\|:----------\|:-------------------\|:-------------\|:----------------\|:--------------\|:-----------------\|:--------------------------\|:------------------------------\|:----------------------\|:-------------------\|:----------------\|:------------\|:--------\|:-----------\|:---------------------------\|:--------------\|:-----------\|:-------------------\|:------\|:----------------\|:-------------------\|:----------------\|:-----------------\|:----------------\|:-------\|:---------------\|:---------------\|:---------------\| \| 0 \| 24 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 22 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| X \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 36 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 7 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| \| X \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| X \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| \| X \| \| X \| \| \| X \| X \| \| \| \| X \| \| \| \| \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
AlekseyKorshuk/dalio-book-handwritten-io-sorted	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string splits: - name: train num_bytes: 674482.0 num_examples: 442 - name: validation num_bytes: 519665 num_examples: 315 - name: test num_bytes: 14786 num_examples: 10 download_size: 0 dataset_size: 1208933.0 --- # Dataset Card for "dalio-book-handwritten-io-sorted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mainlp/inconsistencies_companies	--- license: cc-by-4.0 ---
Mr-User/many	--- license: apache-2.0 ---
echarlaix/vqa	--- license: apache-2.0 ---
satanicsmores/IMAGE2IMAGE-SatTANIC-SMores	--- license: mit language: - en - gl - is tags: - art - code - not-for-all-audiences pretty_name: pretty_name size_categories: - n<1K ---
amansahanigermany/cartoonizer-dataset	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 6497455.0 num_examples: 10 download_size: 6500440 dataset_size: 6497455.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ttxy/kaggle	--- license: apache-2.0 --- kaggle datasets
Megnis/ml_talents_hr-llamma2-style	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 7708951 num_examples: 656 download_size: 2288316 dataset_size: 7708951 configs: - config_name: default data_files: - split: train path: data/train-* ---
NeuraXenetica/managpt-4080-nlp-prompts-and-generated-texts	--- license: cc-by-4.0 task_categories: - text-generation language: - en pretty_name: 'ManaGPT: 4,080 NLP prompts and generated texts' size_categories: - 1K<n<10K --- This dataset includes 4,080 texts that were generated by the [ManaGPT-1020](https://huggingface.co/NeuraXenetica/ManaGPT-1020) large language model, in response to particular input sequences. ManaGPT-1020 is a free, open-source model available for download and use via Hugging Face’s “transformers” Python package. The model is a 1.5-billion-parameter LLM that’s capable of generating text in order to complete a sentence whose first words have been provided via a user-supplied input sequence. The model represents an elaboration of GPT-2 that has been fine-tuned (using Python and TensorFlow) on a specialized English-language corpus of over 509,000 words from the domain of organizational futures studies. In particular, the model has been trained to generate analysis, predictions, and recommendations regarding the emerging role of advanced AI, social robotics, ubiquitous computing, virtual reality, neurocybernetic augmentation, and other “posthumanizing” technologies in organizational life. In generating the texts, 102 different prompts were used, each of which was employed to generate 20 responses. The 102 input sequences were created by concatenating 12 different "subjects" with 17 different "modal variants," in every possible combination. The subjects included 6 grammatically singular subjects: - "The workplace of tomorrow" - "Technological posthumanization" - "The organizational use of AI" - "A robotic boss" - "An artificially intelligent coworker" - "Business culture within Society 5.0" Also included were 6 grammatically plural subjects: - "Social robots" - "Hybrid human-robotic organizations" - "Artificially intelligent businesses" - "The posthumanized workplaces of the future" - "Cybernetically augmented workers" - "Organizations in Society 5.0" For the 6 grammatically singular subjects, the 17 modal variants included one "blank" variant (an empty string) and 16 phrases that lend the input sequence diverse forms of "modal shading," by indicating varying degrees of certainty, probability, predictability, logical necessity, or moral obligation or approbation. These modal variants were: - "" - " is" - " is not" - " will" - " will be" - " may" - " might never" - " is likely to" - " is unlikely to" - " should" - " can" - " cannot" - " can never" - " must" - " must not" - " is like" - " will be like" The variants used with grammatically plural subjects were identical, apart from the fact that the word “is” was changed to “are,” wherever it appeared. In a small number of cases (only occurring when the empty string "" was used as part of the input sequence), the model failed to generate any output beyond the input sequence itself.
kz919/open-orca-flan-50k-synthetic-reward-e5-mistral-7b-instruct-v7	--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: task dtype: string - name: ignos-Mistral-T5-7B-v1 dtype: string - name: cognAI-lil-c3po dtype: string - name: viethq188-Rabbit-7B-DPO-Chat dtype: string - name: cookinai-DonutLM-v1 dtype: string - name: v1olet-v1olet-merged-dpo-7B dtype: string - name: normalized_rewards sequence: float64 - name: router_label dtype: int64 splits: - name: train num_bytes: 16166616 num_examples: 7271 download_size: 7453727 dataset_size: 16166616 configs: - config_name: default data_files: - split: train path: data/train-* ---
cakiki/stack-smol-xxl-embeddings	--- dataset_info: features: - name: token_ids sequence: int64 - name: lri_160 sequence: float64 splits: - name: train num_bytes: 231978165104 num_examples: 11658586 download_size: 34909750705 dataset_size: 231978165104 --- # Dataset Card for "stack-smol-xxl-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-emotion-2d469b4f-13675887	--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: [] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
RustamovPY/test_voices	--- dataset_info: features: - name: file_name dtype: string - name: voice dtype: string - name: text dtype: string - name: speaker dtype: string splits: - name: train num_bytes: 385 num_examples: 3 download_size: 2746 dataset_size: 385 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test_voices" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/dioscuri_pollux_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Dioscuri Pollux (Fate/Grand Order) This is the dataset of Dioscuri Pollux (Fate/Grand Order), containing 131 images and their tags. The core tags of this character are `blonde_hair, bangs, breasts, medium_hair, blue_eyes, small_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:---------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 131 \| 152.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 131 \| 98.72 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 311 \| 202.78 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 131 \| 136.96 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 311 \| 266.95 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_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/dioscuri_pollux_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 \| 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, armlet, bare_shoulders, diadem, looking_at_viewer, metal_collar, pauldrons, solo, white_robe, halterneck, thighs, black_shirt, bracer, sword, closed_mouth, faulds, simple_background \| \| 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, armlet, bare_shoulders, blush, bracer, covered_navel, diadem, halterneck, looking_at_viewer, medium_breasts, simple_background, solo, thighs, white_background, metal_collar, purple_eyes, smile, closed_mouth, white_robe, faulds \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, armlet, black_shirt, diadem, looking_at_viewer, metal_collar, short_hair, twins, white_robe, 1boy, bare_shoulders, brother_and_sister, simple_background, white_background, pauldrons, halterneck, smile \| \| 3 \| 17 \| ![](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) \| 1boy, 1girl, blush, hetero, nipples, thighs, diadem, large_breasts, open_mouth, collarbone, penis, armlet, bar_censor, pussy, vaginal, bare_shoulders, girl_on_top, nude, purple_eyes, sex_from_behind, smile, speech_bubble, straddling \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| armlet \| bare_shoulders \| diadem \| looking_at_viewer \| metal_collar \| pauldrons \| solo \| white_robe \| halterneck \| thighs \| black_shirt \| bracer \| sword \| closed_mouth \| faulds \| simple_background \| blush \| covered_navel \| medium_breasts \| white_background \| purple_eyes \| smile \| short_hair \| twins \| 1boy \| brother_and_sister \| hetero \| nipples \| large_breasts \| open_mouth \| collarbone \| penis \| bar_censor \| pussy \| vaginal \| girl_on_top \| nude \| sex_from_behind \| speech_bubble \| straddling \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:-----------------\|:---------\|:--------------------\|:---------------\|:------------\|:-------\|:-------------\|:-------------\|:---------\|:--------------\|:---------\|:--------\|:---------------\|:---------\|:--------------------\|:--------\|:----------------\|:-----------------\|:-------------------\|:--------------\|:--------\|:-------------\|:--------\|:-------\|:---------------------\|:---------\|:----------\|:----------------\|:-------------\|:-------------\|:--------\|:-------------\|:--------\|:----------\|:--------------\|:-------\|:------------------\|:----------------\|:-------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| X \| \| \| \| \| X \| \| \| \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 17 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| X \| X \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
HydraLM/GPTeacher-General-Instruct_list_dict	--- dataset_info: features: - name: conversations list: - name: input dtype: string - name: instruction dtype: string - name: response dtype: string - name: conversation_id dtype: int64 splits: - name: train num_bytes: 56447404 num_examples: 89259 download_size: 0 dataset_size: 56447404 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPTeacher-General-Instruct_list_dict" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
presencesw/cot-collection_v2	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: rationale dtype: string - name: task dtype: string - name: type dtype: string splits: - name: train num_bytes: 1427400995.4030628 num_examples: 1271010 download_size: 546101259 dataset_size: 1427400995.4030628 configs: - config_name: default data_files: - split: train path: data/train-* ---
AntoineBlanot/mnli-3way	--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label_name dtype: string splits: - name: train num_bytes: 75405059 num_examples: 392702 - name: test num_bytes: 1853683 num_examples: 9815 download_size: 51216284 dataset_size: 77258742 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "mnli-3way" This dataset is the [multi_nli](https://huggingface.co/datasets/multi_nli) dataset where the labels are: `entailment`, `contradiction` and `neutral`. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jlbaker361/tex_inv_hot_ip_light	--- dataset_info: features: - name: label dtype: string - name: tex_inv_hot_ip_prompt_similarity dtype: float32 - name: tex_inv_hot_ip_identity_consistency dtype: float32 - name: tex_inv_hot_ip_negative_prompt_similarity dtype: float32 - name: tex_inv_hot_ip_target_prompt_similarity dtype: float32 - name: tex_inv_hot_ip_aesthetic_score dtype: float32 splits: - name: train num_bytes: 308 num_examples: 11 download_size: 4221 dataset_size: 308 configs: - config_name: default data_files: - split: train path: data/train-* ---
amttl	--- annotations_creators: - crowdsourced language_creators: - found language: - zh license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - parsing pretty_name: AMTTL dataset_info: config_name: amttl features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: class_label: names: '0': B '1': I '2': E '3': S splits: - name: train num_bytes: 1132196 num_examples: 3063 - name: validation num_bytes: 324358 num_examples: 822 - name: test num_bytes: 328509 num_examples: 908 download_size: 274351 dataset_size: 1785063 configs: - config_name: amttl data_files: - split: train path: amttl/train-* - split: validation path: amttl/validation-* - split: test path: amttl/test-* default: true --- # Dataset Card for AMTTL ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - Repository: [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - Paper: [Aclweb](http://aclweb.org/anthology/C18-1307) - 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 ```bibtex @inproceedings{xing2018adaptive, title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text}, author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian}, booktitle={Proceedings of the 27th International Conference on Computational Linguistics}, pages={3619--3630}, year={2018} } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.
leoozyhg/12312123	--- license: openrail ---
GAIR/preference-dissection	--- dataset_info: features: - name: query dtype: string - name: scenario_auto-j dtype: string - name: scenario_group dtype: string - name: response_1 struct: - name: content dtype: string - name: model dtype: string - name: num_words dtype: int64 - name: response_2 struct: - name: content dtype: string - name: model dtype: string - name: num_words dtype: int64 - name: gpt-4-turbo_reference dtype: string - name: clear intent dtype: string - name: explicitly express feelings dtype: string - name: explicit constraints sequence: string - name: explicit subjective stances sequence: string - name: explicit mistakes or biases sequence: string - name: preference_labels struct: - name: gpt-3.5-turbo-1106 dtype: string - name: gpt-4-1106-preview dtype: string - name: human dtype: string - name: llama-2-13b dtype: string - name: llama-2-13b-chat dtype: string - name: llama-2-70b dtype: string - name: llama-2-70b-chat dtype: string - name: llama-2-7b dtype: string - name: llama-2-7b-chat dtype: string - name: mistral-7b dtype: string - name: mistral-7b-instruct-v0.1 dtype: string - name: mistral-7b-instruct-v0.2 dtype: string - name: mistral-8x7b dtype: string - name: mistral-8x7b-instruct-v0.1 dtype: string - name: qwen-14b dtype: string - name: qwen-14b-chat dtype: string - name: qwen-72b dtype: string - name: qwen-72b-chat dtype: string - name: qwen-7b dtype: string - name: qwen-7b-chat dtype: string - name: tulu-2-dpo-13b dtype: string - name: tulu-2-dpo-70b dtype: string - name: tulu-2-dpo-7b dtype: string - name: vicuna-13b-v1.5 dtype: string - name: vicuna-7b-v1.5 dtype: string - name: wizardLM-13b-v1.2 dtype: string - name: wizardLM-70b-v1.0 dtype: string - name: yi-34b dtype: string - name: yi-34b-chat dtype: string - name: yi-6b dtype: string - name: yi-6b-chat dtype: string - name: zephyr-7b-alpha dtype: string - name: zephyr-7b-beta dtype: string - name: basic_response_1 struct: - name: admit limitations or mistakes dtype: int64 - name: authoritative tone dtype: int64 - name: clear and understandable dtype: int64 - name: complex word usage and sentence structure dtype: int64 - name: friendly dtype: int64 - name: funny and humorous dtype: int64 - name: grammar, spelling, punctuation, and code-switching dtype: int64 - name: harmlessness dtype: int64 - name: information richness without considering inaccuracy dtype: int64 - name: innovative and novel dtype: int64 - name: interactive dtype: int64 - name: metaphors, personification, similes, hyperboles, irony, parallelism dtype: int64 - name: persuade user dtype: int64 - name: polite dtype: int64 - name: relevance without considering inaccuracy dtype: int64 - name: repetitive dtype: int64 - name: step by step solution dtype: int64 - name: use of direct and explicit supporting materials dtype: int64 - name: use of informal expressions dtype: int64 - name: well formatted dtype: int64 - name: basic_response_2 struct: - name: admit limitations or mistakes dtype: int64 - name: authoritative tone dtype: int64 - name: clear and understandable dtype: int64 - name: complex word usage and sentence structure dtype: int64 - name: friendly dtype: int64 - name: funny and humorous dtype: int64 - name: grammar, spelling, punctuation, and code-switching dtype: int64 - name: harmlessness dtype: int64 - name: information richness without considering inaccuracy dtype: int64 - name: innovative and novel dtype: int64 - name: interactive dtype: int64 - name: metaphors, personification, similes, hyperboles, irony, parallelism dtype: int64 - name: persuade user dtype: int64 - name: polite dtype: int64 - name: relevance without considering inaccuracy dtype: int64 - name: repetitive dtype: int64 - name: step by step solution dtype: int64 - name: use of direct and explicit supporting materials dtype: int64 - name: use of informal expressions dtype: int64 - name: well formatted dtype: int64 - name: errors_response_1 struct: - name: applicable or not dtype: string - name: errors list: - name: brief description dtype: string - name: severity dtype: string - name: type dtype: string - name: errors_response_2 struct: - name: applicable or not dtype: string - name: errors list: - name: brief description dtype: string - name: severity dtype: string - name: type dtype: string - name: query-specific_response_1 struct: - name: clarify user intent dtype: int64 - name: correcting explicit mistakes or biases sequence: string - name: satisfying explicit constraints sequence: string - name: showing empathetic dtype: int64 - name: supporting explicit subjective stances sequence: string - name: query-specific_response_2 struct: - name: clarify user intent dtype: int64 - name: correcting explicit mistakes or biases sequence: string - name: satisfying explicit constraints sequence: string - name: showing empathetic dtype: int64 - name: supporting explicit subjective stances sequence: string splits: - name: train num_bytes: 27617371 num_examples: 5240 download_size: 13124269 dataset_size: 27617371 configs: - config_name: default data_files: - split: train path: data/train-* language: - en pretty_name: Preference Dissection license: cc-by-nc-4.0 --- ## Introduction We release the annotated data used in [Dissecting Human and LLM Preferences](https://arxiv.org/abs/2402.11296). Original Dataset - The dataset is based on [lmsys/chatbot_arena_conversations](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations), which contains 33K cleaned conversations with pairwise human preferences collected from 13K unique IP addresses on the [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) from April to June 2023. Filtering and Scenario-wise Sampling - We filter out the conversations that are not in English, with "Tie" or "Both Bad" labels, and the multi-turn conversations. We first sample 400 samples with unsafe queries according to the OpenAI moderation API tags and the additional toxic tags in the original dataset, then we apply [Auto-J's scenario classifier](https://huggingface.co/GAIR/autoj-scenario-classifier) to determine the scenario of each sample (we merge the Auto-J's scenarios into 10 new ones). For the Knowledge-aware and Others scenarios, we pick 820 samples, and for the other scenarios, we pick 400 samples. The total number is 5,240. Collecting Preferences - Besides the human preference labels in this original dataset, we also collect the binary preference labels from 32 LLMs, including 2 proprietary LLMs and 30 open-source ones. Annotation on Defined Properties - We define a set of 29 properties, we annotate how each property is satisfied (in Likert scale rating or property-specific annotation) in all responses ($5,240\times 2=10,480$). See our paper for more details of the defined properties. ## Dataset Overview An example of the json format is as follows: ```json { "query": "...", "scenario_auto-j": "...", "scenario_group": "...", "response_1": { "content": "...", "model": "...", "num_words": "..." }, "response_2": {...}, "gpt-4-turbo_reference": "...", "clear intent": "Yes/No", "explicitly express feelings": "Yes/No", "explicit constraints": [ ... ], "explicit subjective stances": [ ... ], "explicit mistakes or biases": [ ... ], "preference_labels": { "human": "response_1/response_2", "gpt-4-turbo": "response_1/response_2", ... }, "basic_response_1": { "admit limitations or mistakes": 0/1/2/3, "authoritative tone": 0/1/2/3, ... }, "basic_response_2": {...}, "errors_response_1": { "applicable or not": "applicable/not applicable", "errors":[ { "brief description": "...", "severity": "severe/moderate/minor", "type": "...", }, ... ] }, "errors_response_2": {...}, "query-specific_response_1": { "clarify user intent": ..., "correcting explicit mistakes or biases": None, "satisfying explicit constraints": [ ... ], "showing empathetic": [ ... ], "supporting explicit subjective stances": [ ... ] }, "query-specific_response_2": {...} } ``` The following fields are basic information: - query: The user query. - scenario_auto-j: The scenario classified by Auto-J's classifier. - scenario_group: One of the 10 new scenarios we merged from the Auto-J's scenario, including an Unsafe Query scenario. - response_1/response_2: The content of a response: - content: The text content. - model: The model that generate this response. - num_words: The number of words of this response, determined by NLTK. - gpt-4-turbo_reference: An reference response generated by GPT-4-Turbo. The following fields are Query-Specific prerequisites. For the last three, there may be an empty list if there is no constraints/stances/mistakes. - clear intent: Whether the intent of the user is clearly expressed in the query, "Yes" or "No". - explicitly express feelings: Whether the user clearly express his/her feelings or emotions in the query, "Yes" or "No". - explicit constraints": A list containing all the explicit constraints in the query. - explicit subjective stances: A list containing all the subjective stances in the query. - explicit mistakes or biases: A list containing all the mistakes or biases in the query. The following fields are the main body of the annotation. - preference_labels: The preference label for each judge (human or an LLM) indicating which response is preferred in a pair, "response_1/response_2". - basic_response_1/basic_response_2: The annotated ratings of the 20 basic properties (except lengthy) for the response. - property_name: 0/1/2/3 - ... - errors_response_1/errors_response_2: The detected errors of the response. - applicable or not: If GPT-4-Turbo find itself can reliably detect the errors in the response. - errors: A list containing the detected errors in the response. - brief description: A brief description of the error. - severity: How much the error affect the overall correctness of the response, "severe/moderate/minor". - type: The type of the error, "factual error/information contradiction to the query/math operation error/code generation error" - query-specific_response_1/query-specific_response_2: The annotation results of the Query-Specific properties. - clarify user intent: If the user intent is not clear, rate how much the response help clarify the intent, 0/1/2/3. - showing empathetic: If the user expresses feelings or emotions, rate how much the response show empathetic, 0/1/2/3. - satisfying explicit constraints: If there are explicit constraints in the query, rate how much the response satisfy each of them. - A list of "{description of constraint} \| 0/1/2/3" - correcting explicit mistakes or biases: If there are mistakes of biases in the query, classify how the response correct each of them - A list of "{description of mistake} \| Pointed out and corrected/Pointed out but not corrected/Corrected without being pointed out/Neither pointed out nor corrected" - supporting explicit subjective stances: If there are subject stances in the query, classify how the response support each of them - A list of "{description of stance} \| Strongly supported/Weakly supported/Neutral/Weakly opposed/Strongly opposed" ## Statistics 👇 Number of samples meeting 5 Query-specific prerequisites. \| Prerequisite \| # \| Prerequisite \| # \| \| ------------------------- \| ----- \| ---------------- \| ---- \| \| with explicit constraints \| 1,418 \| unclear intent \| 459 \| \| show subjective stances \| 388 \| express feelings \| 121 \| \| contain mistakes or bias \| 401 \| \| \| 👇 Mean Score/Count for each property in collected data. The average scores of 5 query-specific properties are calculated only on samples where the queries met specific prerequisites. \| Property \| Mean Score/Count \| Property \| Mean Score/Count \| \| ---------------------------- \| ---------------- \| ---------------------------- \| ---------------- \| \| Mean Score* \| \| \| harmless \| 2.90 \| persuasive \| 0.27 \| \| grammarly correct \| 2.70 \| step-by-step \| 0.37 \| \| friendly \| 1.79 \| use informal expressions \| 0.04 \| \| polite \| 2.78 \| clear \| 2.54 \| \| interactive \| 0.22 \| contain rich information \| 1.74 \| \| authoritative \| 1.67 \| novel \| 0.47 \| \| funny \| 0.08 \| relevant \| 2.45 \| \| use rhetorical devices \| 0.16 \| clarify intent* \| 1.33 \| \| complex word & sentence \| 0.89 \| show empathetic* \| 1.48 \| \| use supporting materials \| 0.13 \| satisfy constraints* \| 2.01 \| \| well formatted \| 1.26 \| support stances* \| 2.28 \| \| admit limits \| 0.17 \| correct mistakes* \| 1.08 \| \| Mean Count \| \| \| severe errors \| 0.59 \| minor errors \| 0.23 \| \| moderate errors \| 0.61 \| length \| 164.52 \| 👇 Property correlation in the annotated data. <img src="./property_corr.PNG" alt="image-20240213145030747" style="zoom: 50%;" /> ## Disclaimers and Terms *This part is copied from the original dataset - This dataset contains conversations that may be considered unsafe, offensive, or upsetting. It is not intended for training dialogue agents without applying appropriate filtering measures. We are not responsible for any outputs of the models trained on this dataset. - Statements or opinions made in this dataset do not reflect the views of researchers or institutions involved in the data collection effort. - Users of this data are responsible for ensuring its appropriate use, which includes abiding by any applicable laws and regulations. - Users of this data should adhere to the terms of use for a specific model when using its direct outputs. - Users of this data agree to not attempt to determine the identity of individuals in this dataset. ## License Following the original dataset, this dataset is licensed under CC-BY-NC-4.0. ## Citation ``` @article{li2024dissecting, title={Dissecting Human and LLM Preferences}, author={Li, Junlong and Zhou, Fan and Sun, Shichao and Zhang, Yikai and Zhao, Hai and Liu, Pengfei}, journal={arXiv preprint arXiv:2402.11296}, year={2024} } ```
the-french-artist/wikipedia_20220301.simple_sentence_embeddings	--- license: apache-2.0 dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: sentence_index dtype: int64 - name: line_index dtype: int64 - name: embedding sequence: float32 splits: - name: train num_bytes: 1061056337 num_examples: 585455 download_size: 1331697726 dataset_size: 1061056337 configs: - config_name: default data_files: - split: train path: data/train-* ---
YUiCHl/scale0	--- dataset_info: features: - name: image dtype: image - name: conditioning dtype: image - name: caption dtype: string splits: - name: train num_bytes: 474801995.0 num_examples: 1588 download_size: 472199271 dataset_size: 474801995.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
hopee4/cariuchas	--- license: openrail ---
gagan3012/arabictext	--- dataset_info: features: - name: text dtype: string - name: check_char_repetition_criteria dtype: float64 - name: check_flagged_words_criteria dtype: float64 - name: meta_data dtype: string - name: __id__ dtype: int64 - name: duplicate dtype: bool - name: char dtype: int64 - name: Arabic_char dtype: int64 - name: latin_char dtype: int64 - name: numbers_char dtype: int64 - name: puc_char dtype: int64 splits: - name: train num_bytes: 471530 num_examples: 1000 download_size: 231211 dataset_size: 471530 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "arabictext" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/autotrain-data-ner-test	Invalid username or password.
cyzgab/singlish-to-english-synthetic	--- license: cc-by-nc-sa-4.0 task_categories: - translation language: - en pretty_name: Singlish to English 🇸🇬 size_categories: - n<1K --- # Singlish to English 🇸🇬 > Singapore is known for its efficiency and Singlish is no different - it's colourful and snappy. - [Tessa Wong, BBC News, 2015](https://www.bbc.com/news/magazine-33809914) This is a synthetic dataset generated by GPT-4. Each json pair contains one Singlish sentence about an everyday activity (e.g. cooking) and its English translation. # Sample entry ```json singlish: "Eh, chop the garlic - you can a not?", english: Hey, do you know how to chop the garlic?" ``` # Data Generation Code ```python import json import pandas as pd from openai import OpenAI client = OpenAI() NUM_SAMPLE = 10 ACTIVITIES = ['cooking', 'studying', 'sleeping', 'eating', 'working', 'exercising', 'reading', 'cleaning', 'shopping', 'driving', 'walking', 'bathing', 'going to work', 'listening to music', 'watching TV', 'playing video games', 'using a computer', 'texting', 'socializing', 'meditating', 'commuting', 'doing laundry', 'ironing clothes', 'dusting', 'vacuuming', 'painting', 'drawing', 'grocery shopping', 'sewing', 'taking a nap', 'jogging', 'biking', 'swimming', 'playing sports', 'checking emails', 'playing with children', 'watching movies', 'playing board games', 'attending school or classes', 'going to the gym', 'playing a musical instrument', 'singing', 'dancing', 'writing', 'photography', 'traveling', 'visiting friends', 'attending events', 'volunteering', 'attending meetings'] dataset = {} for index, activity in enumerate(ACTIVITIES): print(index, activity) response = client.chat.completions.create( model="gpt-4-1106-preview", messages=[{"role": "system", "content": "You are an expert in translating Singlish to English"}, {"role": "user", "content": f"Create {NUM_SAMPLE} random Singlish (s) to English (e) translation pairs in json. Write full sentences about {activity}."\ f"Don't exaggerate the use of Singlish, and be natural, as how a real Singaporean would speak."\ f"Start the keys from {(index*NUM_SAMPLE)+1}. For example,"\ "{'X':{'s': 'aiyo, why like that', 'e': 'oh my, how did this happen'}"\ "..., 'X+5': {'s': 'don't play play', 'e': 'don't fool around'} }"}], temperature=0.01, response_format={"type":"json_object"} ) output = response.choices[0].message.content output_json = json.loads(output) dataset.update(output_json) # Save the current state of the combined dictionary with open('singlish_to_english_v0.1.json', 'w') as f: json.dump(dataset, f, indent=None) # Convert to tabular csv df = pd.read_json("singlish_to_english_v0.1.json") df = df.T df = df.reset_index() df.columns = ["index", "singlish", "english"] df.to_csv("singlish_to_english_v0.1.csv", index=False) ```
open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1	--- pretty_name: Evaluation run of Replete-AI/Mistral-Evolved-11b-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Replete-AI/Mistral-Evolved-11b-v0.1](https://huggingface.co/Replete-AI/Mistral-Evolved-11b-v0.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T15:15:50.568647](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1/blob/main/results_2024-03-21T15-15-50.568647.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\": 0.6309829529704917,\n\ \ \"acc_stderr\": 0.03249502226073932,\n \"acc_norm\": 0.6345615860197364,\n\ \ \"acc_norm_stderr\": 0.033137530512338635,\n \"mc1\": 0.4320685434516524,\n\ \ \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5923114451952954,\n\ \ \"mc2_stderr\": 0.016045963776594944\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5989761092150171,\n \"acc_stderr\": 0.014322255790719867,\n\ \ \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.014169664520303101\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6646086436964748,\n\ \ \"acc_stderr\": 0.004711622011148463,\n \"acc_norm\": 0.8465445130452102,\n\ \ \"acc_norm_stderr\": 0.0035968938961909113\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.042446332383532265\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.55,\n\ \ \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6415094339622641,\n \"acc_stderr\": 0.02951470358398176,\n\ \ \"acc_norm\": 0.6415094339622641,\n \"acc_norm_stderr\": 0.02951470358398176\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4298245614035088,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.024251071262208837,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.024251071262208837\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\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\": 0.8,\n \"acc_stderr\": 0.031234752377721164,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721164\n \ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.02805779167298901,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.02805779167298901\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131154,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131154\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163224,\n \"\ acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163224\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"\ acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437395,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437395\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159274,\n \ \ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159274\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7085201793721974,\n\ \ \"acc_stderr\": 0.03050028317654585,\n \"acc_norm\": 0.7085201793721974,\n\ \ \"acc_norm_stderr\": 0.03050028317654585\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\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\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077816,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077816\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8007662835249042,\n\ \ \"acc_stderr\": 0.014283378044296417,\n \"acc_norm\": 0.8007662835249042,\n\ \ \"acc_norm_stderr\": 0.014283378044296417\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.20446927374301677,\n\ \ \"acc_stderr\": 0.01348881340471193,\n \"acc_norm\": 0.20446927374301677,\n\ \ \"acc_norm_stderr\": 0.01348881340471193\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n\ \ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.025494259350694905,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.025494259350694905\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.02532988817190092,\n\ \ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.02532988817190092\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\": 0.4680573663624511,\n\ \ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\ \ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6985294117647058,\n \"acc_stderr\": 0.027875982114273168,\n\ \ \"acc_norm\": 0.6985294117647058,\n \"acc_norm_stderr\": 0.027875982114273168\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6372549019607843,\n \"acc_stderr\": 0.01945076843250552,\n \ \ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.01945076843250552\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.02950489645459596,\n\ \ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.02950489645459596\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.02740385941078684,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.02740385941078684\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338734,\n\ \ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338734\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4320685434516524,\n\ \ \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5923114451952954,\n\ \ \"mc2_stderr\": 0.016045963776594944\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174789\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.4981046247156937,\n \ \ \"acc_stderr\": 0.013772385765569753\n }\n}\n```" repo_url: https://huggingface.co/Replete-AI/Mistral-Evolved-11b-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|arc:challenge\|25_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-21T15-15-50.568647.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|gsm8k\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hellaswag\|10_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T15-15-50.568647.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T15-15-50.568647.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '/details_harness\|winogrande\|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-21T15-15-50.568647.parquet' - config_name: results data_files: - split: 2024_03_21T15_15_50.568647 path: - results_2024-03-21T15-15-50.568647.parquet - split: latest path: - results_2024-03-21T15-15-50.568647.parquet --- # Dataset Card for Evaluation run of Replete-AI/Mistral-Evolved-11b-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Replete-AI/Mistral-Evolved-11b-v0.1](https://huggingface.co/Replete-AI/Mistral-Evolved-11b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T15:15:50.568647](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1/blob/main/results_2024-03-21T15-15-50.568647.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": 0.6309829529704917, "acc_stderr": 0.03249502226073932, "acc_norm": 0.6345615860197364, "acc_norm_stderr": 0.033137530512338635, "mc1": 0.4320685434516524, "mc1_stderr": 0.017341202394988257, "mc2": 0.5923114451952954, "mc2_stderr": 0.016045963776594944 }, "harness\|arc:challenge\|25": { "acc": 0.5989761092150171, "acc_stderr": 0.014322255790719867, "acc_norm": 0.6220136518771331, "acc_norm_stderr": 0.014169664520303101 }, "harness\|hellaswag\|10": { "acc": 0.6646086436964748, "acc_stderr": 0.004711622011148463, "acc_norm": 0.8465445130452102, "acc_norm_stderr": 0.0035968938961909113 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5925925925925926, "acc_stderr": 0.042446332383532265, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.042446332383532265 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395268, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395268 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.55, "acc_stderr": 0.04999999999999999, "acc_norm": 0.55, "acc_norm_stderr": 0.04999999999999999 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6415094339622641, "acc_stderr": 0.02951470358398176, "acc_norm": 0.6415094339622641, "acc_norm_stderr": 0.02951470358398176 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958548, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958548 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594963, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594963 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.025591857761382182, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.025591857761382182 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7612903225806451, "acc_stderr": 0.024251071262208837, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.024251071262208837 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "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": 0.8, "acc_stderr": 0.031234752377721164, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721164 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8080808080808081, "acc_stderr": 0.02805779167298901, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.02805779167298901 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8652849740932642, "acc_stderr": 0.02463978909770944, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.02463978909770944 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131154, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131154 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977934, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977934 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8385321100917431, "acc_stderr": 0.015776239256163224, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.015776239256163224 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5509259259259259, "acc_stderr": 0.03392238405321617, "acc_norm": 0.5509259259259259, "acc_norm_stderr": 0.03392238405321617 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437395, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437395 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7637130801687764, "acc_stderr": 0.027652153144159274, "acc_norm": 0.7637130801687764, "acc_norm_stderr": 0.027652153144159274 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7085201793721974, "acc_stderr": 0.03050028317654585, "acc_norm": 0.7085201793721974, "acc_norm_stderr": 0.03050028317654585 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "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": 0.7484662576687117, "acc_stderr": 0.034089978868575295, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.034089978868575295 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077816, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077816 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8007662835249042, "acc_stderr": 0.014283378044296417, "acc_norm": 0.8007662835249042, "acc_norm_stderr": 0.014283378044296417 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.20446927374301677, "acc_stderr": 0.01348881340471193, "acc_norm": 0.20446927374301677, "acc_norm_stderr": 0.01348881340471193 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694905, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694905 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7067901234567902, "acc_stderr": 0.02532988817190092, "acc_norm": 0.7067901234567902, "acc_norm_stderr": 0.02532988817190092 }, "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": 0.4680573663624511, "acc_stderr": 0.012744149704869649, "acc_norm": 0.4680573663624511, "acc_norm_stderr": 0.012744149704869649 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6985294117647058, "acc_stderr": 0.027875982114273168, "acc_norm": 0.6985294117647058, "acc_norm_stderr": 0.027875982114273168 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6372549019607843, "acc_stderr": 0.01945076843250552, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.01945076843250552 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6938775510204082, "acc_stderr": 0.02950489645459596, "acc_norm": 0.6938775510204082, "acc_norm_stderr": 0.02950489645459596 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8159203980099502, "acc_stderr": 0.02740385941078684, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.02740385941078684 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.783625730994152, "acc_stderr": 0.03158149539338734, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.03158149539338734 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4320685434516524, "mc1_stderr": 0.017341202394988257, "mc2": 0.5923114451952954, "mc2_stderr": 0.016045963776594944 }, "harness\|winogrande\|5": { "acc": 0.7576953433307024, "acc_stderr": 0.012042352526174789 }, "harness\|gsm8k\|5": { "acc": 0.4981046247156937, "acc_stderr": 0.013772385765569753 } } ``` ## 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]
kavyamanohar/Malayalam-word-freq	--- license: cc-by-4.0 --- ## Word Frequency Profile of Malayalam The repo contains Malayalam words and their frequencies as obtained from AI4Bharat [Indic NLP corpus](https://github.com/AI4Bharat/indicnlp_corpus). There is an associated python script to plot the word frequnecy profile.
hemantk089/llama2_7b_fine_tuning_complete_dataset_v7	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 301722 num_examples: 813 - name: test num_bytes: 72617 num_examples: 204 download_size: 107905 dataset_size: 374339 --- # Dataset Card for "llama2_7b_fine_tuning_complete_dataset_v7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/koshigaya_natsumi_nonnonbiyori	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Koshigaya Natsumi This is the dataset of Koshigaya Natsumi, containing 300 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 \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 737 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 824 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 737 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 737 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 604 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 824 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 824 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
Code-Refinement/utf_20_refs	--- dataset_info: features: - name: problem_id dtype: string - name: attempt_id dtype: int64 - name: generated_solution dtype: string - name: original_reward dtype: float64 - name: chosen_ref_id dtype: int64 - name: chosen_refinement dtype: string - name: chosen_reward dtype: float64 - name: rejected_ref_id dtype: int64 - name: rejected_refinement dtype: string - name: rejected_reward dtype: float64 splits: - name: train num_bytes: 439039919 num_examples: 303459 - name: test num_bytes: 116366494 num_examples: 68155 download_size: 17812330 dataset_size: 555406413 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

LuangMV97/EmpathetiCounseling_Previo

--- dataset_info: features: - name: input dtype: string - name: label dtype: string splits: - name: train num_bytes: 13883804.45384577 num_examples: 47998 - name: test num_bytes: 5771364.976021614 num_examples: 12002 download_size: 13237318 dataset_size: 19655169.429867383 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

cjvt/sentinews

--- annotations_creators: - crowdsourced language: - sl language_creators: - found license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: SentiNews size_categories: [] source_datasets: - original tags: - slovenian sentiment - news articles task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for SentiNews ## Dataset Description - **Homepage:** https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene - **Paper:** Bučar, J., Žnidaršič, M. & Povh, J. Annotated news corpora and a lexicon for sentiment analysis in Slovene. Lang Resources & Evaluation 52, 895–919 (2018). https://doi.org/10.1007/s10579-018-9413-3 ### Dataset Summary SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their sentiment by between two and six annotators. It is annotated at three granularities: - document-level (config `document_level`, 10 427 documents), - paragraph-level (config `paragraph_level`, 89 999 paragraphs), and - sentence-level (config `sentence_level`, 168 899 sentences). ### Supported Tasks and Leaderboards Sentiment classification, three classes (negative, neutral, positive). ### Languages Slovenian. ## Dataset Structure ### Data Instances A sample instance from the sentence-level config: ``` { 'nid': 2, 'content': 'Vilo Prešeren je na dražbi ministrstva za obrambo kupilo nepremičninsko podjetje Condor Real s sedežem v Lescah.', 'sentiment': 'neutral', 'pid': 1, 'sid': 1 } ``` ### Data Fields The data fields are similar among all three configs, with the only difference being the IDs. - `nid`: a uint16 containing a unique ID of the news article (document). - `content`: a string containing the body of the news article - `sentiment`: the sentiment of the instance - `pid`: a uint8 containing the consecutive number of the paragraph inside the current news article, **not unique** (present in the configs `paragraph_level` and `sentence_level`) - `sid`: a uint8 containing the consecutive number of the sentence inside the current paragraph, **not unique** (present in the config `sentence_level`) ## Additional Information ### Dataset Curators Jože Bučar, Martin Žnidaršič, Janez Povh. ### Licensing Information CC BY-SA 4.0 ### Citation Information ``` @article{buvcar2018annotated, title={Annotated news corpora and a lexicon for sentiment analysis in Slovene}, author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez}, journal={Language Resources and Evaluation}, volume={52}, number={3}, pages={895--919}, year={2018}, publisher={Springer} } ``` ### Contributions Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

TheFinAI/flare-fomc

--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: text dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: test num_bytes: 384180 num_examples: 496 download_size: 140144 dataset_size: 384180 --- # Dataset Card for "flare-fomc" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

r0ll/kussia

--- license: openrail language: - ru --- Voice https://www.twitch.tv/kussia88 RVC v2 350 epoch

open-llm-leaderboard/details_smelborp__MixtralOrochi8x7B

--- pretty_name: Evaluation run of smelborp/MixtralOrochi8x7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B)\ \ 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_smelborp__MixtralOrochi8x7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-30T02:24:04.286101](https://huggingface.co/datasets/open-llm-leaderboard/details_smelborp__MixtralOrochi8x7B/blob/main/results_2023-12-30T02-24-04.286101.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\": 0.6933650457336848,\n\ \ \"acc_stderr\": 0.030427906094644037,\n \"acc_norm\": 0.7040806942153237,\n\ \ \"acc_norm_stderr\": 0.03106416581410797,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6399397085586839,\n\ \ \"mc2_stderr\": 0.015220747814252549\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6655290102389079,\n \"acc_stderr\": 0.013787460322441379,\n\ \ \"acc_norm\": 0.7030716723549488,\n \"acc_norm_stderr\": 0.013352025976725225\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6814379605656243,\n\ \ \"acc_stderr\": 0.00464966527389064,\n \"acc_norm\": 0.8609838677554272,\n\ \ \"acc_norm_stderr\": 0.0034525630964691227\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742399,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742399\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7697368421052632,\n \"acc_stderr\": 0.03426059424403165,\n\ \ \"acc_norm\": 0.7697368421052632,\n \"acc_norm_stderr\": 0.03426059424403165\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7886792452830189,\n \"acc_stderr\": 0.02512576648482785,\n\ \ \"acc_norm\": 0.7886792452830189,\n \"acc_norm_stderr\": 0.02512576648482785\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565656,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565656\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n\ \ \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7021276595744681,\n \"acc_stderr\": 0.02989614568209546,\n\ \ \"acc_norm\": 0.7021276595744681,\n \"acc_norm_stderr\": 0.02989614568209546\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5526315789473685,\n\ \ \"acc_stderr\": 0.046774730044912,\n \"acc_norm\": 0.5526315789473685,\n\ \ \"acc_norm_stderr\": 0.046774730044912\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n\ \ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.5211640211640212,\n \"acc_stderr\": 0.025728230952130723,\n \"\ acc_norm\": 0.5211640211640212,\n \"acc_norm_stderr\": 0.025728230952130723\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5317460317460317,\n\ \ \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.5317460317460317,\n\ \ \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8387096774193549,\n \"acc_stderr\": 0.020923327006423298,\n \"\ acc_norm\": 0.8387096774193549,\n \"acc_norm_stderr\": 0.020923327006423298\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5960591133004927,\n \"acc_stderr\": 0.03452453903822032,\n \"\ acc_norm\": 0.5960591133004927,\n \"acc_norm_stderr\": 0.03452453903822032\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8535353535353535,\n \"acc_stderr\": 0.025190921114603918,\n \"\ acc_norm\": 0.8535353535353535,\n \"acc_norm_stderr\": 0.025190921114603918\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9481865284974094,\n \"acc_stderr\": 0.01599622932024412,\n\ \ \"acc_norm\": 0.9481865284974094,\n \"acc_norm_stderr\": 0.01599622932024412\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8025210084033614,\n \"acc_stderr\": 0.025859164122051453,\n\ \ \"acc_norm\": 0.8025210084033614,\n \"acc_norm_stderr\": 0.025859164122051453\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4370860927152318,\n \"acc_stderr\": 0.04050035722230636,\n \"\ acc_norm\": 0.4370860927152318,\n \"acc_norm_stderr\": 0.04050035722230636\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8807339449541285,\n \"acc_stderr\": 0.013895729292588957,\n \"\ acc_norm\": 0.8807339449541285,\n \"acc_norm_stderr\": 0.013895729292588957\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5694444444444444,\n \"acc_stderr\": 0.03376922151252335,\n \"\ acc_norm\": 0.5694444444444444,\n \"acc_norm_stderr\": 0.03376922151252335\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8725490196078431,\n \"acc_stderr\": 0.023405530480846315,\n \"\ acc_norm\": 0.8725490196078431,\n \"acc_norm_stderr\": 0.023405530480846315\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n \ \ \"acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7668161434977578,\n\ \ \"acc_stderr\": 0.02838039114709471,\n \"acc_norm\": 0.7668161434977578,\n\ \ \"acc_norm_stderr\": 0.02838039114709471\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525975,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525975\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622793,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622793\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.03083349114628123,\n\ \ \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.03083349114628123\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\ \ \"acc_stderr\": 0.01831589168562586,\n \"acc_norm\": 0.9145299145299145,\n\ \ \"acc_norm_stderr\": 0.01831589168562586\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909282\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8722860791826309,\n\ \ \"acc_stderr\": 0.011935626313999874,\n \"acc_norm\": 0.8722860791826309,\n\ \ \"acc_norm_stderr\": 0.011935626313999874\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044283,\n\ \ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044283\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.464804469273743,\n\ \ \"acc_stderr\": 0.016681020931076655,\n \"acc_norm\": 0.464804469273743,\n\ \ \"acc_norm_stderr\": 0.016681020931076655\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.0231527224394023,\n\ \ \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.0231527224394023\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8117283950617284,\n \"acc_stderr\": 0.021751866060815875,\n\ \ \"acc_norm\": 0.8117283950617284,\n \"acc_norm_stderr\": 0.021751866060815875\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5212765957446809,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.5212765957446809,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5260756192959583,\n\ \ \"acc_stderr\": 0.012752858346533143,\n \"acc_norm\": 0.5260756192959583,\n\ \ \"acc_norm_stderr\": 0.012752858346533143\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.025187786660227255,\n\ \ \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.025187786660227255\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7369281045751634,\n \"acc_stderr\": 0.017812676542320657,\n \ \ \"acc_norm\": 0.7369281045751634,\n \"acc_norm_stderr\": 0.017812676542320657\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8040816326530612,\n \"acc_stderr\": 0.025409301953225678,\n\ \ \"acc_norm\": 0.8040816326530612,\n \"acc_norm_stderr\": 0.025409301953225678\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.032659863237109066,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.032659863237109066\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072878,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072878\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6399397085586839,\n\ \ \"mc2_stderr\": 0.015220747814252549\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7987371744277821,\n \"acc_stderr\": 0.011268519971577682\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1728582259287339,\n \ \ \"acc_stderr\": 0.010415432246200583\n }\n}\n```" repo_url: https://huggingface.co/smelborp/MixtralOrochi8x7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|arc:challenge|25_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-30T02-24-04.286101.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|gsm8k|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hellaswag|10_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-24-04.286101.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-24-04.286101.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T02-24-04.286101.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_30T02_24_04.286101 path: - '**/details_harness|winogrande|5_2023-12-30T02-24-04.286101.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-30T02-24-04.286101.parquet' - config_name: results data_files: - split: 2023_12_30T02_24_04.286101 path: - results_2023-12-30T02-24-04.286101.parquet - split: latest path: - results_2023-12-30T02-24-04.286101.parquet --- # Dataset Card for Evaluation run of smelborp/MixtralOrochi8x7B  Dataset automatically created during the evaluation run of model [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B) 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_smelborp__MixtralOrochi8x7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-30T02:24:04.286101](https://huggingface.co/datasets/open-llm-leaderboard/details_smelborp__MixtralOrochi8x7B/blob/main/results_2023-12-30T02-24-04.286101.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": 0.6933650457336848, "acc_stderr": 0.030427906094644037, "acc_norm": 0.7040806942153237, "acc_norm_stderr": 0.03106416581410797, "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6399397085586839, "mc2_stderr": 0.015220747814252549 }, "harness|arc:challenge|25": { "acc": 0.6655290102389079, "acc_stderr": 0.013787460322441379, "acc_norm": 0.7030716723549488, "acc_norm_stderr": 0.013352025976725225 }, "harness|hellaswag|10": { "acc": 0.6814379605656243, "acc_stderr": 0.00464966527389064, "acc_norm": 0.8609838677554272, "acc_norm_stderr": 0.0034525630964691227 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742399, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742399 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7697368421052632, "acc_stderr": 0.03426059424403165, "acc_norm": 0.7697368421052632, "acc_norm_stderr": 0.03426059424403165 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7886792452830189, "acc_stderr": 0.02512576648482785, "acc_norm": 0.7886792452830189, "acc_norm_stderr": 0.02512576648482785 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565656, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565656 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7021276595744681, "acc_stderr": 0.02989614568209546, "acc_norm": 0.7021276595744681, "acc_norm_stderr": 0.02989614568209546 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5526315789473685, "acc_stderr": 0.046774730044912, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.046774730044912 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419036, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419036 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5211640211640212, "acc_stderr": 0.025728230952130723, "acc_norm": 0.5211640211640212, "acc_norm_stderr": 0.025728230952130723 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5317460317460317, "acc_stderr": 0.04463112720677173, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677173 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8387096774193549, "acc_stderr": 0.020923327006423298, "acc_norm": 0.8387096774193549, "acc_norm_stderr": 0.020923327006423298 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5960591133004927, "acc_stderr": 0.03452453903822032, "acc_norm": 0.5960591133004927, "acc_norm_stderr": 0.03452453903822032 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8535353535353535, "acc_stderr": 0.025190921114603918, "acc_norm": 0.8535353535353535, "acc_norm_stderr": 0.025190921114603918 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9481865284974094, "acc_stderr": 0.01599622932024412, "acc_norm": 0.9481865284974094, "acc_norm_stderr": 0.01599622932024412 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948496, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948496 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8025210084033614, "acc_stderr": 0.025859164122051453, "acc_norm": 0.8025210084033614, "acc_norm_stderr": 0.025859164122051453 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4370860927152318, "acc_stderr": 0.04050035722230636, "acc_norm": 0.4370860927152318, "acc_norm_stderr": 0.04050035722230636 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8807339449541285, "acc_stderr": 0.013895729292588957, "acc_norm": 0.8807339449541285, "acc_norm_stderr": 0.013895729292588957 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5694444444444444, "acc_stderr": 0.03376922151252335, "acc_norm": 0.5694444444444444, "acc_norm_stderr": 0.03376922151252335 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8725490196078431, "acc_stderr": 0.023405530480846315, "acc_norm": 0.8725490196078431, "acc_norm_stderr": 0.023405530480846315 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8734177215189873, "acc_stderr": 0.021644195727955173, "acc_norm": 0.8734177215189873, "acc_norm_stderr": 0.021644195727955173 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7668161434977578, "acc_stderr": 0.02838039114709471, "acc_norm": 0.7668161434977578, "acc_norm_stderr": 0.02838039114709471 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8091603053435115, "acc_stderr": 0.034465133507525975, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.034465133507525975 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622793, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 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0.04292346959909282, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8722860791826309, "acc_stderr": 0.011935626313999874, "acc_norm": 0.8722860791826309, "acc_norm_stderr": 0.011935626313999874 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7427745664739884, "acc_stderr": 0.023532925431044283, "acc_norm": 0.7427745664739884, "acc_norm_stderr": 0.023532925431044283 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.464804469273743, "acc_stderr": 0.016681020931076655, "acc_norm": 0.464804469273743, "acc_norm_stderr": 0.016681020931076655 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7941176470588235, "acc_stderr": 0.0231527224394023, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.0231527224394023 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8117283950617284, "acc_stderr": 0.021751866060815875, "acc_norm": 0.8117283950617284, "acc_norm_stderr": 0.021751866060815875 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5212765957446809, "acc_stderr": 0.029800481645628693, "acc_norm": 0.5212765957446809, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5260756192959583, "acc_stderr": 0.012752858346533143, "acc_norm": 0.5260756192959583, "acc_norm_stderr": 0.012752858346533143 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7794117647058824, "acc_stderr": 0.025187786660227255, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.025187786660227255 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7369281045751634, "acc_stderr": 0.017812676542320657, "acc_norm": 0.7369281045751634, "acc_norm_stderr": 0.017812676542320657 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302505, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302505 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8040816326530612, "acc_stderr": 0.025409301953225678, "acc_norm": 0.8040816326530612, "acc_norm_stderr": 0.025409301953225678 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101706, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.032659863237109066, "acc_norm": 0.88, "acc_norm_stderr": 0.032659863237109066 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072878, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072878 }, "harness|truthfulqa:mc|0": { "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6399397085586839, "mc2_stderr": 0.015220747814252549 }, "harness|winogrande|5": { "acc": 0.7987371744277821, "acc_stderr": 0.011268519971577682 }, "harness|gsm8k|5": { "acc": 0.1728582259287339, "acc_stderr": 0.010415432246200583 } } ``` ## 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]

Athiwat/brain2

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 5590698.0 num_examples: 5 download_size: 5591635 dataset_size: 5590698.0 --- # Dataset Card for "brain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

davidgaofc/d_shadow_inout

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 927049 num_examples: 3280 download_size: 371018 dataset_size: 927049 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/shiki_eiki_touhou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of shiki_eiki/四季映姫・ヤマザナドゥ/四季映姫/시키에이키야마자나두 (Touhou) This is the dataset of shiki_eiki/四季映姫・ヤマザナドゥ/四季映姫/시키에이키야마자나두 (Touhou), containing 500 images and their tags. The core tags of this character are `green_hair, hat, short_hair, blue_eyes, ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:------------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 601.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 366.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1145 | 750.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 543.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1145 | 1018.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiki_eiki_touhou/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/shiki_eiki_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blue_vest, long_sleeves, solo, white_shirt, frilled_hat, looking_at_viewer, simple_background, bangs, epaulettes, holding, rod_of_remorse, white_background, blue_headwear, black_skirt, blush, bow, open_mouth, upper_body, closed_mouth | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_footwear, black_skirt, blue_vest, full_body, long_sleeves, ribbon-trimmed_skirt, rod_of_remorse, solo, white_shirt, asymmetrical_hair, bangs, blue_headwear, frilled_hat, holding, white_socks, closed_mouth, looking_at_viewer, red_bow, epaulettes, white_ribbon, footwear_bow, red_ribbon, standing, white_background, white_bow, simple_background, buttons, green_eyes, mary_janes | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bangs, black_skirt, blue_vest, cowboy_shot, juliet_sleeves, looking_at_viewer, red_ribbon, ribbon-trimmed_skirt, rod_of_remorse, solo, wide_sleeves, blush, closed_mouth, epaulettes, holding, white_shirt, frilled_hat, frilled_skirt, hair_between_eyes, red_bow, smile, white_ribbon, black_background, blue_headwear, green_eyes, hat_ribbon, spider_lily, standing | | 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, hat_ribbon, shirt, solo, vest, looking_at_viewer, rod_of_remorse, skirt, juliet_sleeves, spider_lily, open_mouth, petals, wide_sleeves | | 4 | 7 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, black_thighhighs, rod_of_remorse, skirt, solo, wide_sleeves, hat_ribbon, long_sleeves, zettai_ryouiki, vest, green_eyes | | 5 | 9 | ![](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, solo, rod_of_remorse, upper_body, blush, looking_at_viewer | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, adapted_costume, black_thighhighs, detached_sleeves, skirt, solo, alternate_costume, bare_shoulders, blush, bow, looking_at_viewer, magical_girl, rod_of_remorse, smile, zettai_ryouiki, asymmetrical_hair, hat_ribbon, open_mouth, boots, frills | | 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) | 1boy, 1girl, blush, hetero, mosaic_censoring, open_mouth, penis, sex, solo_focus, vaginal, bangs, blue_vest, cum_in_pussy, frilled_hat, nipples, on_back, white_shirt, blue_headwear, feet_out_of_frame, long_sleeves, looking_at_viewer, missionary, alternate_breast_size, black_skirt, bow, breast_grab, clothing_aside, cum_on_breasts, grabbing, hair_between_eyes, huge_breasts, navel, panties, pov, spread_legs | | 8 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, hetero, open_mouth, penis, sex, solo_focus, vaginal, nipples, 1boy, blush, cum_in_pussy, cowgirl_position, girl_on_top, navel, nude, bangs, flat_chest, frilled_hat, mosaic_censoring, sweat, bar_censor, hair_between_eyes, heart, large_breasts, looking_at_viewer, tears, thighhighs | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, blush, looking_at_viewer, navel, nipples, pussy, solo, standing, bangs, frilled_hat, open_mouth, simple_background, small_breasts, white_background, ass_visible_through_thighs, censored, completely_nude, asymmetrical_hair, collarbone, cowboy_shot, green_eyes, hair_between_eyes, red_ribbon, thigh_gap, white_ribbon | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_vest | long_sleeves | solo | white_shirt | frilled_hat | looking_at_viewer | simple_background | bangs | epaulettes | holding | rod_of_remorse | white_background | blue_headwear | black_skirt | blush | bow | open_mouth | upper_body | closed_mouth | black_footwear | full_body | ribbon-trimmed_skirt | asymmetrical_hair | white_socks | red_bow | white_ribbon | footwear_bow | red_ribbon | standing | white_bow | buttons | green_eyes | mary_janes | cowboy_shot | juliet_sleeves | wide_sleeves | frilled_skirt | hair_between_eyes | smile | black_background | hat_ribbon | spider_lily | shirt | vest | skirt | petals | black_thighhighs | zettai_ryouiki | adapted_costume | detached_sleeves | alternate_costume | bare_shoulders | magical_girl | boots | frills | 1boy | hetero | mosaic_censoring | penis | sex | solo_focus | vaginal | cum_in_pussy | nipples | on_back | feet_out_of_frame | missionary | alternate_breast_size | breast_grab | clothing_aside | cum_on_breasts | grabbing | huge_breasts | navel | panties | pov | spread_legs | cowgirl_position | girl_on_top | nude | flat_chest | sweat | bar_censor | heart | large_breasts | tears | thighhighs | pussy | small_breasts | ass_visible_through_thighs | censored | completely_nude | collarbone | thigh_gap | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:---------------|:-------|:--------------|:--------------|:--------------------|:--------------------|:--------|:-------------|:----------|:-----------------|:-------------------|:----------------|:--------------|:--------|:------|:-------------|:-------------|:---------------|:-----------------|:------------|:-----------------------|:--------------------|:--------------|:----------|:---------------|:---------------|:-------------|:-----------|:------------|:----------|:-------------|:-------------|:--------------|:-----------------|:---------------|:----------------|:--------------------|:--------|:-------------------|:-------------|:--------------|:--------|:-------|:--------|:---------|:-------------------|:-----------------|:------------------|:-------------------|:--------------------|:-----------------|:---------------|:--------|:---------|:-------|:---------|:-------------------|:--------|:------|:-------------|:----------|:---------------|:----------|:----------|:--------------------|:-------------|:------------------------|:--------------|:-----------------|:-----------------|:-----------|:---------------|:--------|:----------|:------|:--------------|:-------------------|:--------------|:-------|:-------------|:--------|:-------------|:--------|:----------------|:--------|:-------------|:--------|:----------------|:-----------------------------|:-----------|:------------------|:-------------|:------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | X | X | | X | X | X | X | | X | X | X | | | | X | | | X | | | X | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 7 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | X | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | | | X | | | | | X | | | | X | X | X | | | | | | X | | | | | | | | | | | | | | | | X | | X | | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | | X | X | X | | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 8 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | | | X | X | | X | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | | X | X | X | X | | | | X | | | X | | X | | | | | | X | 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results-sd-v1-5-sd-v2-1-if-v1-0-karlo/9c69e716

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1342 dataset_size: 180 --- # Dataset Card for "9c69e716" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

davanstrien/cards20230512

--- dataset_info: features: - name: url dtype: string - name: card dtype: string splits: - name: train num_bytes: 264313043 num_examples: 202911 download_size: 78441708 dataset_size: 264313043 --- # Dataset Card for "cards20230512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

chiyuanhsiao/ML2021_HungyiLee_Corpus

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: test num_bytes: 1086428751.563 num_examples: 31181 download_size: 1086479549 dataset_size: 1086428751.563 --- # Dataset Card for "debug" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

WarpWingHF/QTRAGPT

--- license: mit ---

sudopop/korean_food

--- license: unknown ---

LexiconShiftInnovations/SinhalaSubtitlesDatasetClean

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 51288853 num_examples: 1149 download_size: 20145527 dataset_size: 51288853 configs: - config_name: default data_files: - split: train path: data/train-* ---

ylacombe/bella_ciao

--- dataset_info: features: - name: audio dtype: audio - name: vocals dtype: audio - name: others dtype: audio splits: - name: train num_bytes: 158140620.0 num_examples: 30 download_size: 156971661 dataset_size: 158140620.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO

--- pretty_name: Evaluation run of Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO](https://huggingface.co/Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO)\ \ 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_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-11T12:02:04.707768](https://huggingface.co/datasets/open-llm-leaderboard/details_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO/blob/main/results_2024-02-11T12-02-04.707768.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\": 0.6083454936984162,\n\ \ \"acc_stderr\": 0.033140017189034275,\n \"acc_norm\": 0.6127945476017843,\n\ \ \"acc_norm_stderr\": 0.0338104933555728,\n \"mc1\": 0.40514075887392903,\n\ \ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5791139392635098,\n\ \ \"mc2_stderr\": 0.015266138543062658\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5665529010238908,\n \"acc_stderr\": 0.014481376224558903,\n\ \ \"acc_norm\": 0.6049488054607508,\n \"acc_norm_stderr\": 0.014285898292938163\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6436964748058156,\n\ \ \"acc_stderr\": 0.004779276329704048,\n \"acc_norm\": 0.8383788090021908,\n\ \ \"acc_norm_stderr\": 0.0036735065123709547\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n\ \ \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\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\"\ : 0.5,\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\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\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\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.032529096196131965,\n\ \ \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.032529096196131965\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\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.38095238095238093,\n \"acc_stderr\": 0.0250107491161376,\n \"\ acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.0250107491161376\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.04343525428949097,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.04343525428949097\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-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\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.0356796977226805,\n\ \ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.0356796977226805\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646826,\n \"\ acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646826\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5717948717948718,\n \"acc_stderr\": 0.025088301454694827,\n\ \ \"acc_norm\": 0.5717948717948718,\n \"acc_norm_stderr\": 0.025088301454694827\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059288,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059288\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7981651376146789,\n \"acc_stderr\": 0.017208579357787586,\n \"\ acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.017208579357787586\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7598039215686274,\n\ \ \"acc_stderr\": 0.02998373305591361,\n \"acc_norm\": 0.7598039215686274,\n\ \ \"acc_norm_stderr\": 0.02998373305591361\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.7552742616033755,\n \"acc_stderr\": 0.027985699387036423,\n\ \ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.027985699387036423\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n\ \ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n\ \ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.036959801280988226,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988226\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.04453197507374984,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.04453197507374984\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.03512385283705048,\n\ \ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.03512385283705048\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.045416094465039504,\n\ \ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.045416094465039504\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.02308663508684141,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.02308663508684141\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7803320561941252,\n\ \ \"acc_stderr\": 0.014805384478371155,\n \"acc_norm\": 0.7803320561941252,\n\ \ \"acc_norm_stderr\": 0.014805384478371155\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.024946792225272314,\n\ \ \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.024946792225272314\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3653631284916201,\n\ \ \"acc_stderr\": 0.01610483388014229,\n \"acc_norm\": 0.3653631284916201,\n\ \ \"acc_norm_stderr\": 0.01610483388014229\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.02633661346904663,\n\ \ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.02633661346904663\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6784565916398714,\n\ \ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\ \ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6851851851851852,\n \"acc_stderr\": 0.025842248700902168,\n\ \ \"acc_norm\": 0.6851851851851852,\n \"acc_norm_stderr\": 0.025842248700902168\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.029736592526424438,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.029736592526424438\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44589308996088656,\n\ \ \"acc_stderr\": 0.012695244711379772,\n \"acc_norm\": 0.44589308996088656,\n\ \ \"acc_norm_stderr\": 0.012695244711379772\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5955882352941176,\n \"acc_stderr\": 0.029812630701569743,\n\ \ \"acc_norm\": 0.5955882352941176,\n \"acc_norm_stderr\": 0.029812630701569743\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6111111111111112,\n \"acc_stderr\": 0.019722058939618068,\n \ \ \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.019722058939618068\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\ \ \"acc_stderr\": 0.04265792110940588,\n \"acc_norm\": 0.7272727272727273,\n\ \ \"acc_norm_stderr\": 0.04265792110940588\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\ \ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233257,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233257\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5,\n \ \ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \"\ acc_norm_stderr\": 0.03892494720807614\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40514075887392903,\n\ \ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5791139392635098,\n\ \ \"mc2_stderr\": 0.015266138543062658\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836676\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4177407126611069,\n \ \ \"acc_stderr\": 0.013584820638504832\n }\n}\n```" repo_url: https://huggingface.co/Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO 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_11T12_02_04.707768 path: - '**/details_harness|arc:challenge|25_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-11T12-02-04.707768.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|gsm8k|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hellaswag|10_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-11T12-02-04.707768.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-management|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T12-02-04.707768.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|truthfulqa:mc|0_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-11T12-02-04.707768.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_11T12_02_04.707768 path: - '**/details_harness|winogrande|5_2024-02-11T12-02-04.707768.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-11T12-02-04.707768.parquet' - config_name: results data_files: - split: 2024_02_11T12_02_04.707768 path: - results_2024-02-11T12-02-04.707768.parquet - split: latest path: - results_2024-02-11T12-02-04.707768.parquet --- # Dataset Card for Evaluation run of Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO  Dataset automatically created during the evaluation run of model [Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO](https://huggingface.co/Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO) 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_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-11T12:02:04.707768](https://huggingface.co/datasets/open-llm-leaderboard/details_Radu1999__Mistral-Instruct-Ukrainian-SFT-DPO/blob/main/results_2024-02-11T12-02-04.707768.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": 0.6083454936984162, "acc_stderr": 0.033140017189034275, "acc_norm": 0.6127945476017843, "acc_norm_stderr": 0.0338104933555728, "mc1": 0.40514075887392903, "mc1_stderr": 0.01718561172775337, "mc2": 0.5791139392635098, "mc2_stderr": 0.015266138543062658 }, "harness|arc:challenge|25": { "acc": 0.5665529010238908, "acc_stderr": 0.014481376224558903, "acc_norm": 0.6049488054607508, "acc_norm_stderr": 0.014285898292938163 }, "harness|hellaswag|10": { "acc": 0.6436964748058156, "acc_stderr": 0.004779276329704048, "acc_norm": 0.8383788090021908, "acc_norm_stderr": 0.0036735065123709547 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.047609522856952365, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952365 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.631578947368421, "acc_stderr": 0.03925523381052932, "acc_norm": 0.631578947368421, "acc_norm_stderr": 0.03925523381052932 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "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": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.548936170212766, "acc_stderr": 0.032529096196131965, "acc_norm": 0.548936170212766, "acc_norm_stderr": 0.032529096196131965 }, "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": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.38095238095238093, "acc_stderr": 0.0250107491161376, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.0250107491161376 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949097, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949097 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "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": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.703030303030303, "acc_stderr": 0.0356796977226805, "acc_norm": 0.703030303030303, "acc_norm_stderr": 0.0356796977226805 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7424242424242424, "acc_stderr": 0.031156269519646826, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.031156269519646826 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8652849740932642, "acc_stderr": 0.02463978909770944, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.02463978909770944 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5717948717948718, "acc_stderr": 0.025088301454694827, "acc_norm": 0.5717948717948718, "acc_norm_stderr": 0.025088301454694827 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028597 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6470588235294118, "acc_stderr": 0.031041941304059288, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059288 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.0386155754625517, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.0386155754625517 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7981651376146789, "acc_stderr": 0.017208579357787586, "acc_norm": 0.7981651376146789, "acc_norm_stderr": 0.017208579357787586 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7598039215686274, "acc_stderr": 0.02998373305591361, "acc_norm": 0.7598039215686274, "acc_norm_stderr": 0.02998373305591361 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7552742616033755, "acc_stderr": 0.027985699387036423, "acc_norm": 0.7552742616033755, "acc_norm_stderr": 0.027985699387036423 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6143497757847534, "acc_stderr": 0.03266842214289201, "acc_norm": 0.6143497757847534, "acc_norm_stderr": 0.03266842214289201 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.036959801280988226, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.036959801280988226 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6944444444444444, "acc_stderr": 0.04453197507374984, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.04453197507374984 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7239263803680982, "acc_stderr": 0.03512385283705048, "acc_norm": 0.7239263803680982, "acc_norm_stderr": 0.03512385283705048 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness|hendrycksTest-management|5": { "acc": 0.6990291262135923, "acc_stderr": 0.045416094465039504, "acc_norm": 0.6990291262135923, "acc_norm_stderr": 0.045416094465039504 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8547008547008547, "acc_stderr": 0.02308663508684141, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.02308663508684141 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7803320561941252, "acc_stderr": 0.014805384478371155, "acc_norm": 0.7803320561941252, "acc_norm_stderr": 0.014805384478371155 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6878612716763006, "acc_stderr": 0.024946792225272314, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.024946792225272314 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3653631284916201, "acc_stderr": 0.01610483388014229, "acc_norm": 0.3653631284916201, "acc_norm_stderr": 0.01610483388014229 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.696078431372549, "acc_stderr": 0.02633661346904663, "acc_norm": 0.696078431372549, "acc_norm_stderr": 0.02633661346904663 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6851851851851852, "acc_stderr": 0.025842248700902168, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.025842248700902168 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.029736592526424438, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.029736592526424438 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44589308996088656, "acc_stderr": 0.012695244711379772, "acc_norm": 0.44589308996088656, "acc_norm_stderr": 0.012695244711379772 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5955882352941176, "acc_stderr": 0.029812630701569743, "acc_norm": 0.5955882352941176, "acc_norm_stderr": 0.029812630701569743 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6111111111111112, "acc_stderr": 0.019722058939618068, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.019722058939618068 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940588, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940588 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7020408163265306, "acc_stderr": 0.029279567411065677, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.029279567411065677 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233257, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233257 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-virology|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727668, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727668 }, "harness|truthfulqa:mc|0": { "mc1": 0.40514075887392903, "mc1_stderr": 0.01718561172775337, "mc2": 0.5791139392635098, "mc2_stderr": 0.015266138543062658 }, "harness|winogrande|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836676 }, "harness|gsm8k|5": { "acc": 0.4177407126611069, "acc_stderr": 0.013584820638504832 } } ``` ## 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]

bcui19/UltraMix

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 108959181 num_examples: 41457 download_size: 54577284 dataset_size: 108959181 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "UltraMix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vic0428/imdb-card-pred-scientific

--- dataset_info: features: - name: text dtype: string - name: prompt dtype: string - name: true_cardinality dtype: int64 splits: - name: train num_bytes: 39344995.2 num_examples: 80000 - name: test num_bytes: 9836248.8 num_examples: 20000 download_size: 8634654 dataset_size: 49181244.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "imdb-card-pred-scientific" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_adept__persimmon-8b-base

--- pretty_name: Evaluation run of adept/persimmon-8b-base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_adept__persimmon-8b-base\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-10-11T16:30:00.730198](https://huggingface.co/datasets/open-llm-leaderboard/details_adept__persimmon-8b-base/blob/main/results_2023-10-11T16-30-00.730198.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\": 0.4373382174928584,\n\ \ \"acc_stderr\": 0.03537473296886481,\n \"acc_norm\": 0.440779620602171,\n\ \ \"acc_norm_stderr\": 0.03536781150443019,\n \"mc1\": 0.22888616891064872,\n\ \ \"mc1_stderr\": 0.014706994909055027,\n \"mc2\": 0.378505315070287,\n\ \ \"mc2_stderr\": 0.013586954257578736\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.41552901023890787,\n \"acc_stderr\": 0.014401366641216384,\n\ \ \"acc_norm\": 0.4274744027303754,\n \"acc_norm_stderr\": 0.014456862944650652\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5203146783509262,\n\ \ \"acc_stderr\": 0.004985661282998582,\n \"acc_norm\": 0.7114120693089027,\n\ \ \"acc_norm_stderr\": 0.004521798577922143\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.4276315789473684,\n \"acc_stderr\": 0.04026097083296559,\n\ \ \"acc_norm\": 0.4276315789473684,\n \"acc_norm_stderr\": 0.04026097083296559\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.4377358490566038,\n \"acc_stderr\": 0.030533338430467512,\n\ \ \"acc_norm\": 0.4377358490566038,\n \"acc_norm_stderr\": 0.030533338430467512\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5208333333333334,\n\ \ \"acc_stderr\": 0.041775789507399935,\n \"acc_norm\": 0.5208333333333334,\n\ \ \"acc_norm_stderr\": 0.041775789507399935\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n\ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n\ \ \"acc_stderr\": 0.03733626655383509,\n \"acc_norm\": 0.3988439306358382,\n\ \ \"acc_norm_stderr\": 0.03733626655383509\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.03793281185307809,\n\ \ \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.03793281185307809\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.3617021276595745,\n \"acc_stderr\": 0.03141082197596241,\n\ \ \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.03141082197596241\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2804232804232804,\n \"acc_stderr\": 0.023135287974325642,\n \"\ acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.023135287974325642\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.4838709677419355,\n \"acc_stderr\": 0.028429203176724555,\n \"\ acc_norm\": 0.4838709677419355,\n \"acc_norm_stderr\": 0.028429203176724555\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.33004926108374383,\n \"acc_stderr\": 0.033085304262282574,\n \"\ acc_norm\": 0.33004926108374383,\n \"acc_norm_stderr\": 0.033085304262282574\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.5696969696969697,\n \"acc_stderr\": 0.03866225962879077,\n\ \ \"acc_norm\": 0.5696969696969697,\n \"acc_norm_stderr\": 0.03866225962879077\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.5050505050505051,\n \"acc_stderr\": 0.035621707606254015,\n \"\ acc_norm\": 0.5050505050505051,\n \"acc_norm_stderr\": 0.035621707606254015\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.5181347150259067,\n \"acc_stderr\": 0.036060650018329185,\n\ \ \"acc_norm\": 0.5181347150259067,\n \"acc_norm_stderr\": 0.036060650018329185\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.39487179487179486,\n \"acc_stderr\": 0.02478431694215638,\n\ \ \"acc_norm\": 0.39487179487179486,\n \"acc_norm_stderr\": 0.02478431694215638\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340492,\n \ \ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340492\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.39915966386554624,\n \"acc_stderr\": 0.031811100324139245,\n\ \ \"acc_norm\": 0.39915966386554624,\n \"acc_norm_stderr\": 0.031811100324139245\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119994,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119994\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.5321100917431193,\n \"acc_stderr\": 0.021393071222680797,\n \"\ acc_norm\": 0.5321100917431193,\n \"acc_norm_stderr\": 0.021393071222680797\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.2824074074074074,\n \"acc_stderr\": 0.03070137211151094,\n \"\ acc_norm\": 0.2824074074074074,\n \"acc_norm_stderr\": 0.03070137211151094\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5882352941176471,\n \"acc_stderr\": 0.034542365853806094,\n \"\ acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.034542365853806094\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5569620253164557,\n \"acc_stderr\": 0.032335327775334835,\n \ \ \"acc_norm\": 0.5569620253164557,\n \"acc_norm_stderr\": 0.032335327775334835\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.42152466367713004,\n\ \ \"acc_stderr\": 0.03314190222110657,\n \"acc_norm\": 0.42152466367713004,\n\ \ \"acc_norm_stderr\": 0.03314190222110657\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5419847328244275,\n \"acc_stderr\": 0.04369802690578756,\n\ \ \"acc_norm\": 0.5419847328244275,\n \"acc_norm_stderr\": 0.04369802690578756\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.5289256198347108,\n \"acc_stderr\": 0.04556710331269498,\n \"\ acc_norm\": 0.5289256198347108,\n \"acc_norm_stderr\": 0.04556710331269498\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.42592592592592593,\n\ \ \"acc_stderr\": 0.047803436269367894,\n \"acc_norm\": 0.42592592592592593,\n\ \ \"acc_norm_stderr\": 0.047803436269367894\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.588957055214724,\n \"acc_stderr\": 0.038656978537853624,\n\ \ \"acc_norm\": 0.588957055214724,\n \"acc_norm_stderr\": 0.038656978537853624\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3482142857142857,\n\ \ \"acc_stderr\": 0.04521829902833585,\n \"acc_norm\": 0.3482142857142857,\n\ \ \"acc_norm_stderr\": 0.04521829902833585\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.49514563106796117,\n \"acc_stderr\": 0.049505043821289195,\n\ \ \"acc_norm\": 0.49514563106796117,\n \"acc_norm_stderr\": 0.049505043821289195\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6367521367521367,\n\ \ \"acc_stderr\": 0.03150712523091264,\n \"acc_norm\": 0.6367521367521367,\n\ \ \"acc_norm_stderr\": 0.03150712523091264\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5466155810983397,\n\ \ \"acc_stderr\": 0.017802087135850304,\n \"acc_norm\": 0.5466155810983397,\n\ \ \"acc_norm_stderr\": 0.017802087135850304\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.4653179190751445,\n \"acc_stderr\": 0.0268542579282589,\n\ \ \"acc_norm\": 0.4653179190751445,\n \"acc_norm_stderr\": 0.0268542579282589\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24022346368715083,\n\ \ \"acc_stderr\": 0.014288343803925296,\n \"acc_norm\": 0.24022346368715083,\n\ \ \"acc_norm_stderr\": 0.014288343803925296\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.4869281045751634,\n \"acc_stderr\": 0.028620130800700246,\n\ \ \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.028620130800700246\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4758842443729904,\n\ \ \"acc_stderr\": 0.028365041542564577,\n \"acc_norm\": 0.4758842443729904,\n\ \ \"acc_norm_stderr\": 0.028365041542564577\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4660493827160494,\n \"acc_stderr\": 0.027756535257347666,\n\ \ \"acc_norm\": 0.4660493827160494,\n \"acc_norm_stderr\": 0.027756535257347666\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.35815602836879434,\n \"acc_stderr\": 0.028602085862759422,\n \ \ \"acc_norm\": 0.35815602836879434,\n \"acc_norm_stderr\": 0.028602085862759422\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3344198174706649,\n\ \ \"acc_stderr\": 0.012049668983214933,\n \"acc_norm\": 0.3344198174706649,\n\ \ \"acc_norm_stderr\": 0.012049668983214933\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.39705882352941174,\n \"acc_stderr\": 0.029722152099280058,\n\ \ \"acc_norm\": 0.39705882352941174,\n \"acc_norm_stderr\": 0.029722152099280058\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.38562091503267976,\n \"acc_stderr\": 0.01969145905235415,\n \ \ \"acc_norm\": 0.38562091503267976,\n \"acc_norm_stderr\": 0.01969145905235415\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5181818181818182,\n\ \ \"acc_stderr\": 0.04785964010794917,\n \"acc_norm\": 0.5181818181818182,\n\ \ \"acc_norm_stderr\": 0.04785964010794917\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.40408163265306124,\n \"acc_stderr\": 0.03141470802586589,\n\ \ \"acc_norm\": 0.40408163265306124,\n \"acc_norm_stderr\": 0.03141470802586589\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.572139303482587,\n\ \ \"acc_stderr\": 0.03498541988407795,\n \"acc_norm\": 0.572139303482587,\n\ \ \"acc_norm_stderr\": 0.03498541988407795\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.5964912280701754,\n \"acc_stderr\": 0.03762738699917057,\n\ \ \"acc_norm\": 0.5964912280701754,\n \"acc_norm_stderr\": 0.03762738699917057\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22888616891064872,\n\ \ \"mc1_stderr\": 0.014706994909055027,\n \"mc2\": 0.378505315070287,\n\ \ \"mc2_stderr\": 0.013586954257578736\n }\n}\n```" repo_url: https://huggingface.co/adept/persimmon-8b-base leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|arc:challenge|25_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hellaswag|10_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-11T16-30-00.730198.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-management|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T16-30-00.730198.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_11T16_30_00.730198 path: - '**/details_harness|truthfulqa:mc|0_2023-10-11T16-30-00.730198.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-11T16-30-00.730198.parquet' - config_name: results data_files: - split: 2023_10_11T16_30_00.730198 path: - results_2023-10-11T16-30-00.730198.parquet - split: latest path: - results_2023-10-11T16-30-00.730198.parquet --- # Dataset Card for Evaluation run of adept/persimmon-8b-base ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/adept/persimmon-8b-base - **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 [adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_adept__persimmon-8b-base", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-10-11T16:30:00.730198](https://huggingface.co/datasets/open-llm-leaderboard/details_adept__persimmon-8b-base/blob/main/results_2023-10-11T16-30-00.730198.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": 0.4373382174928584, "acc_stderr": 0.03537473296886481, "acc_norm": 0.440779620602171, "acc_norm_stderr": 0.03536781150443019, "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.378505315070287, "mc2_stderr": 0.013586954257578736 }, "harness|arc:challenge|25": { "acc": 0.41552901023890787, "acc_stderr": 0.014401366641216384, "acc_norm": 0.4274744027303754, "acc_norm_stderr": 0.014456862944650652 }, "harness|hellaswag|10": { "acc": 0.5203146783509262, "acc_stderr": 0.004985661282998582, "acc_norm": 0.7114120693089027, "acc_norm_stderr": 0.004521798577922143 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464242, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464242 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4276315789473684, "acc_stderr": 0.04026097083296559, "acc_norm": 0.4276315789473684, "acc_norm_stderr": 0.04026097083296559 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4377358490566038, "acc_stderr": 0.030533338430467512, "acc_norm": 0.4377358490566038, "acc_norm_stderr": 0.030533338430467512 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5208333333333334, "acc_stderr": 0.041775789507399935, "acc_norm": 0.5208333333333334, "acc_norm_stderr": 0.041775789507399935 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3988439306358382, "acc_stderr": 0.03733626655383509, "acc_norm": 0.3988439306358382, "acc_norm_stderr": 0.03733626655383509 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.17647058823529413, "acc_stderr": 0.03793281185307809, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.03793281185307809 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3617021276595745, "acc_stderr": 0.03141082197596241, "acc_norm": 0.3617021276595745, "acc_norm_stderr": 0.03141082197596241 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2804232804232804, "acc_stderr": 0.023135287974325642, "acc_norm": 0.2804232804232804, "acc_norm_stderr": 0.023135287974325642 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377563, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377563 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.4838709677419355, "acc_stderr": 0.028429203176724555, "acc_norm": 0.4838709677419355, "acc_norm_stderr": 0.028429203176724555 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.33004926108374383, "acc_stderr": 0.033085304262282574, "acc_norm": 0.33004926108374383, "acc_norm_stderr": 0.033085304262282574 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5696969696969697, "acc_stderr": 0.03866225962879077, "acc_norm": 0.5696969696969697, "acc_norm_stderr": 0.03866225962879077 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5050505050505051, "acc_stderr": 0.035621707606254015, "acc_norm": 0.5050505050505051, "acc_norm_stderr": 0.035621707606254015 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5181347150259067, "acc_stderr": 0.036060650018329185, "acc_norm": 0.5181347150259067, "acc_norm_stderr": 0.036060650018329185 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.39487179487179486, "acc_stderr": 0.02478431694215638, "acc_norm": 0.39487179487179486, "acc_norm_stderr": 0.02478431694215638 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2851851851851852, "acc_stderr": 0.027528599210340492, "acc_norm": 0.2851851851851852, "acc_norm_stderr": 0.027528599210340492 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.39915966386554624, "acc_stderr": 0.031811100324139245, "acc_norm": 0.39915966386554624, "acc_norm_stderr": 0.031811100324139245 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119994, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119994 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5321100917431193, "acc_stderr": 0.021393071222680797, "acc_norm": 0.5321100917431193, "acc_norm_stderr": 0.021393071222680797 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2824074074074074, "acc_stderr": 0.03070137211151094, "acc_norm": 0.2824074074074074, "acc_norm_stderr": 0.03070137211151094 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5882352941176471, "acc_stderr": 0.034542365853806094, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.034542365853806094 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5569620253164557, "acc_stderr": 0.032335327775334835, "acc_norm": 0.5569620253164557, "acc_norm_stderr": 0.032335327775334835 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.42152466367713004, "acc_stderr": 0.03314190222110657, "acc_norm": 0.42152466367713004, "acc_norm_stderr": 0.03314190222110657 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5419847328244275, "acc_stderr": 0.04369802690578756, "acc_norm": 0.5419847328244275, "acc_norm_stderr": 0.04369802690578756 }, "harness|hendrycksTest-international_law|5": { "acc": 0.5289256198347108, "acc_stderr": 0.04556710331269498, "acc_norm": 0.5289256198347108, "acc_norm_stderr": 0.04556710331269498 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.42592592592592593, "acc_stderr": 0.047803436269367894, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.047803436269367894 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.588957055214724, "acc_stderr": 0.038656978537853624, "acc_norm": 0.588957055214724, "acc_norm_stderr": 0.038656978537853624 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3482142857142857, "acc_stderr": 0.04521829902833585, "acc_norm": 0.3482142857142857, "acc_norm_stderr": 0.04521829902833585 }, "harness|hendrycksTest-management|5": { "acc": 0.49514563106796117, "acc_stderr": 0.049505043821289195, "acc_norm": 0.49514563106796117, "acc_norm_stderr": 0.049505043821289195 }, "harness|hendrycksTest-marketing|5": { "acc": 0.6367521367521367, "acc_stderr": 0.03150712523091264, "acc_norm": 0.6367521367521367, "acc_norm_stderr": 0.03150712523091264 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5466155810983397, "acc_stderr": 0.017802087135850304, "acc_norm": 0.5466155810983397, "acc_norm_stderr": 0.017802087135850304 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.4653179190751445, "acc_stderr": 0.0268542579282589, "acc_norm": 0.4653179190751445, "acc_norm_stderr": 0.0268542579282589 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24022346368715083, "acc_stderr": 0.014288343803925296, "acc_norm": 0.24022346368715083, "acc_norm_stderr": 0.014288343803925296 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.4869281045751634, "acc_stderr": 0.028620130800700246, "acc_norm": 0.4869281045751634, "acc_norm_stderr": 0.028620130800700246 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.4758842443729904, "acc_stderr": 0.028365041542564577, "acc_norm": 0.4758842443729904, "acc_norm_stderr": 0.028365041542564577 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4660493827160494, "acc_stderr": 0.027756535257347666, "acc_norm": 0.4660493827160494, "acc_norm_stderr": 0.027756535257347666 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.35815602836879434, "acc_stderr": 0.028602085862759422, "acc_norm": 0.35815602836879434, "acc_norm_stderr": 0.028602085862759422 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3344198174706649, "acc_stderr": 0.012049668983214933, "acc_norm": 0.3344198174706649, "acc_norm_stderr": 0.012049668983214933 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.39705882352941174, "acc_stderr": 0.029722152099280058, "acc_norm": 0.39705882352941174, "acc_norm_stderr": 0.029722152099280058 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.38562091503267976, "acc_stderr": 0.01969145905235415, "acc_norm": 0.38562091503267976, "acc_norm_stderr": 0.01969145905235415 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5181818181818182, "acc_stderr": 0.04785964010794917, "acc_norm": 0.5181818181818182, "acc_norm_stderr": 0.04785964010794917 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.40408163265306124, "acc_stderr": 0.03141470802586589, "acc_norm": 0.40408163265306124, "acc_norm_stderr": 0.03141470802586589 }, "harness|hendrycksTest-sociology|5": { "acc": 0.572139303482587, "acc_stderr": 0.03498541988407795, "acc_norm": 0.572139303482587, "acc_norm_stderr": 0.03498541988407795 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-virology|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.5964912280701754, "acc_stderr": 0.03762738699917057, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.03762738699917057 }, "harness|truthfulqa:mc|0": { "mc1": 0.22888616891064872, "mc1_stderr": 0.014706994909055027, "mc2": 0.378505315070287, "mc2_stderr": 0.013586954257578736 } } ``` ### 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]

tr416/dataset_20231006_200728

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 762696.0 num_examples: 297 - name: test num_bytes: 7704.0 num_examples: 3 download_size: 74080 dataset_size: 770400.0 --- # Dataset Card for "dataset_20231006_200728" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

c01dsnap/MaliciousPEs

--- license: other --- # Dataset Description Detailed description: [www.kaggle.com/competitions/malware-classification/overview/description](https://www.kaggle.com/competitions/malware-classification/overview/description) Warning: this dataset is almost half a terabyte uncompressed! We have compressed the data using 7zip to achieve the smallest file size possible. Note that the rules do not allow sharing of the data outside of Kaggle, including bit torrent ([why not?](https://www.kaggle.com/wiki/ANoteOnTorrents)). You are provided with a set of known malware files representing a mix of 9 different families. Each malware file has an Id, a 20 character hash value uniquely identifying the file, and a Class, an integer representing one of 9 family names to which the malware may belong: * Ramnit * Lollipop * Kelihos_ver3 * Vundo * Simda * Tracur * Kelihos_ver1 * Obfuscator.ACY * Gatak For each file, the raw data contains the hexadecimal representation of the file's binary content, without the PE header (to ensure sterility). You are also provided a metadata manifest, which is a log containing various metadata information extracted from the binary, such as function calls, strings, etc. This was generated using the IDA disassembler tool. Your task is to develop the best mechanism for classifying files in the test set into their respective family affiliations. The dataset contains the following files: * train.7z - the raw data for the training set (MD5 hash = 4fedb0899fc2210a6c843889a70952ed) * trainLabels.csv - the class labels associated with the training set * test.7z - the raw data for the test set (MD5 hash = 84b6fbfb9df3c461ed2cbbfa371ffb43) * sampleSubmission.csv - a file showing the valid submission format * dataSample.csv - a sample of the dataset to preview before downloading

EleutherAI/fake-cifarnet

--- dataset_info: features: - name: img dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck splits: - name: train num_bytes: 1827528011.0 num_examples: 190000 - name: test num_bytes: 96682029.0 num_examples: 10000 download_size: 1924310386 dataset_size: 1924210040.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- This is a dataset of "fake" CIFARNet images which were sampled from a high-entropy distribution whose mean and covariance matrix matches that of the original CIFARNet. It was generated with the following code: ```py from datasets import ClassLabel, Dataset, DatasetDict, Features, Image, load_dataset from functools import partial def generator(split: str): from datasets import Dataset from concept_erasure import assert_type, groupby, optimal_linear_shrinkage from concept_erasure.optimal_transport import psd_sqrt from PIL import Image as PilImage from torch import nn, optim, Tensor import torch def koleo(x: Tensor) -> Tensor: """Kozachenko-Leonenko estimator of entropy.""" return torch.cdist(x, x).kthvalue(2).values.log().mean() def hypercube_sample( n: int, mean: Tensor, cov: Tensor, *, koleo_weight: float = 1e-3, max_iter: int = 100, seed: int = 0, ): """Generate `n` samples from a distribution on [0, 1]^d with the given moments.""" d = mean.shape[-1] assert d == cov.shape[-1] == cov.shape[-2], "Dimension mismatch" assert n > 1, "Need at least two samples to compute covariance" eps = torch.finfo(mean.dtype).eps rng = torch.Generator(device=mean.device).manual_seed(seed) # Initialize with max-ent samples matching `mean` and `cov` but without hypercube # constraint. We do so in a way that is robust to singular `cov` z = mean.new_empty([n, d]).normal_(generator=rng) x = torch.clamp(z @ psd_sqrt(cov) + mean, eps, 1 - eps) # Reparametrize to enforce hypercube constraint z = nn.Parameter(x.logit()) opt = optim.LBFGS([z], line_search_fn="strong_wolfe", max_iter=max_iter) def closure(): opt.zero_grad() x = z.sigmoid() loss = torch.norm(x.mean(0) - mean) + torch.norm(x.T.cov() - cov) loss -= koleo_weight * koleo(x) loss.backward() return float(loss) opt.step(closure) return z.sigmoid().detach() ds = assert_type(Dataset, load_dataset("EleutherAI/cifarnet", split=split)) with ds.formatted_as("torch"): X = assert_type(Tensor, ds["image"]).div(255).cuda() Y = assert_type(Tensor, ds["label"]).cuda() # Iterate over the classes for y, x in groupby(X, Y): mean = x.flatten(1).mean(0) cov = optimal_linear_shrinkage(x.flatten(1).mT.cov(), len(x)) for fake_x in hypercube_sample(len(x), mean, cov).reshape_as(x).mul(255).cpu(): yield {"image": PilImage.fromarray(fake_x.numpy()).convert("RGB"), "label": y} features = Features({ "image": Image(), "label": ClassLabel(num_classes=10), }) fake_train = Dataset.from_generator(partial(generator, "train"), features) fake_test = Dataset.from_generator(partial(generator, "test"), features) fake = DatasetDict({"train": fake_train, "test": fake_test}) fake.push_to_hub("EleutherAI/fake-cifarnet") ```

LightFury9/ASR_mini1

--- dataset_info: features: - name: transcription dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 227312957.0 num_examples: 750 download_size: 186500925 dataset_size: 227312957.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

jmayank23/mini-platypus

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 6945089 num_examples: 3000 download_size: 3719748 dataset_size: 6945089 configs: - config_name: default data_files: - split: train path: data/train-* ---

NagendraHarish/mini-platypus

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245921 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_eldogbbhed__NeuralMonarchCoderPearlBeagle

--- pretty_name: Evaluation run of eldogbbhed/NeuralMonarchCoderPearlBeagle dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [eldogbbhed/NeuralMonarchCoderPearlBeagle](https://huggingface.co/eldogbbhed/NeuralMonarchCoderPearlBeagle)\ \ 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_eldogbbhed__NeuralMonarchCoderPearlBeagle\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-02T13:11:13.485539](https://huggingface.co/datasets/open-llm-leaderboard/details_eldogbbhed__NeuralMonarchCoderPearlBeagle/blob/main/results_2024-03-02T13-11-13.485539.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\": 0.6489879256686817,\n\ \ \"acc_stderr\": 0.03206960586209805,\n \"acc_norm\": 0.6497805245984531,\n\ \ \"acc_norm_stderr\": 0.03271953340550891,\n \"mc1\": 0.4565483476132191,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6119293421385199,\n\ \ \"mc2_stderr\": 0.015342412171122335\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620199,\n\ \ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.01357265770308495\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6900019916351324,\n\ \ \"acc_stderr\": 0.0046154722103160396,\n \"acc_norm\": 0.8722366062537343,\n\ \ \"acc_norm_stderr\": 0.0033314391934060397\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.032400380867927465,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.032400380867927465\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\ \ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\ \ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6068965517241379,\n \"acc_stderr\": 0.0407032901370707,\n\ \ \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.0407032901370707\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"\ acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\"\ : 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\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\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n\ \ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047711\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026705,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026705\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097113,\n \ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097113\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524575,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524575\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887037,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887037\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931038,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931038\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.02636165166838909,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.02636165166838909\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.039418975265163025,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.039418975265163025\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.01987565502786746,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.01987565502786746\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\ \ \"acc_stderr\": 0.013625556907993452,\n \"acc_norm\": 0.8237547892720306,\n\ \ \"acc_norm_stderr\": 0.013625556907993452\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069367,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069367\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.37318435754189944,\n\ \ \"acc_stderr\": 0.016175692013381957,\n \"acc_norm\": 0.37318435754189944,\n\ \ \"acc_norm_stderr\": 0.016175692013381957\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242557,\n\ \ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242557\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\ \ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.7234726688102894,\n\ \ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303952,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303952\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170595,\n\ \ \"acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170595\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6617647058823529,\n \"acc_stderr\": 0.019139943748487036,\n \ \ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.019139943748487036\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712844,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712844\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578327,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4565483476132191,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6119293421385199,\n\ \ \"mc2_stderr\": 0.015342412171122335\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938282\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6702047005307051,\n \ \ \"acc_stderr\": 0.012949955030571154\n }\n}\n```" repo_url: https://huggingface.co/eldogbbhed/NeuralMonarchCoderPearlBeagle 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_03_02T13_11_13.485539 path: - '**/details_harness|arc:challenge|25_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-02T13-11-13.485539.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|gsm8k|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hellaswag|10_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-02T13-11-13.485539.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-management|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T13-11-13.485539.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|truthfulqa:mc|0_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-02T13-11-13.485539.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_02T13_11_13.485539 path: - '**/details_harness|winogrande|5_2024-03-02T13-11-13.485539.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-02T13-11-13.485539.parquet' - config_name: results data_files: - split: 2024_03_02T13_11_13.485539 path: - results_2024-03-02T13-11-13.485539.parquet - split: latest path: - results_2024-03-02T13-11-13.485539.parquet --- # Dataset Card for Evaluation run of eldogbbhed/NeuralMonarchCoderPearlBeagle  Dataset automatically created during the evaluation run of model [eldogbbhed/NeuralMonarchCoderPearlBeagle](https://huggingface.co/eldogbbhed/NeuralMonarchCoderPearlBeagle) 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_eldogbbhed__NeuralMonarchCoderPearlBeagle", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-02T13:11:13.485539](https://huggingface.co/datasets/open-llm-leaderboard/details_eldogbbhed__NeuralMonarchCoderPearlBeagle/blob/main/results_2024-03-02T13-11-13.485539.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": 0.6489879256686817, "acc_stderr": 0.03206960586209805, "acc_norm": 0.6497805245984531, "acc_norm_stderr": 0.03271953340550891, "mc1": 0.4565483476132191, "mc1_stderr": 0.017437280953183688, "mc2": 0.6119293421385199, "mc2_stderr": 0.015342412171122335 }, "harness|arc:challenge|25": { "acc": 0.643344709897611, "acc_stderr": 0.013998056902620199, "acc_norm": 0.6851535836177475, "acc_norm_stderr": 0.01357265770308495 }, "harness|hellaswag|10": { "acc": 0.6900019916351324, "acc_stderr": 0.0046154722103160396, "acc_norm": 0.8722366062537343, "acc_norm_stderr": 0.0033314391934060397 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493864, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493864 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.0358687928008034, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.0358687928008034 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.032400380867927465, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.032400380867927465 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6068965517241379, "acc_stderr": 0.0407032901370707, "acc_norm": 0.6068965517241379, "acc_norm_stderr": 0.0407032901370707 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3968253968253968, "acc_stderr": 0.025197101074246483, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.025197101074246483 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.541871921182266, "acc_stderr": 0.03505630140785741, "acc_norm": 0.541871921182266, "acc_norm_stderr": 0.03505630140785741 }, "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": 0.793939393939394, "acc_stderr": 0.03158415324047711, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.03158415324047711 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.02886977846026705, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026705 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709443, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6692307692307692, "acc_stderr": 0.02385479568097113, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.02385479568097113 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524575, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524575 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887037, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887037 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461763, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461763 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5370370370370371, "acc_stderr": 0.03400603625538272, "acc_norm": 0.5370370370370371, "acc_norm_stderr": 0.03400603625538272 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931038, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931038 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7932489451476793, "acc_stderr": 0.02636165166838909, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.02636165166838909 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.035477710041594654, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.035477710041594654 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7520661157024794, "acc_stderr": 0.039418975265163025, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.039418975265163025 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573483, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573483 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7484662576687117, "acc_stderr": 0.034089978868575295, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.034089978868575295 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8974358974358975, "acc_stderr": 0.01987565502786746, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.01987565502786746 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993452, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993452 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069367, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069367 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.37318435754189944, "acc_stderr": 0.016175692013381957, "acc_norm": 0.37318435754189944, "acc_norm_stderr": 0.016175692013381957 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242557, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242557 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7234726688102894, "acc_stderr": 0.02540383297817961, "acc_norm": 0.7234726688102894, "acc_norm_stderr": 0.02540383297817961 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7222222222222222, "acc_stderr": 0.024922001168886335, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.024922001168886335 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303952, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303952 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6948529411764706, "acc_stderr": 0.027971541370170595, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.027971541370170595 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6617647058823529, "acc_stderr": 0.019139943748487036, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.019139943748487036 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.02853556033712844, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712844 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578327, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578327 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness|truthfulqa:mc|0": { "mc1": 0.4565483476132191, "mc1_stderr": 0.017437280953183688, "mc2": 0.6119293421385199, "mc2_stderr": 0.015342412171122335 }, "harness|winogrande|5": { "acc": 0.8050513022888713, "acc_stderr": 0.011134099415938282 }, "harness|gsm8k|5": { "acc": 0.6702047005307051, "acc_stderr": 0.012949955030571154 } } ``` ## 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]

liuyanchen1015/MULTI_VALUE_stsb_regularized_plurals

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 29020 num_examples: 185 - name: test num_bytes: 22752 num_examples: 156 - name: train num_bytes: 91941 num_examples: 572 download_size: 95623 dataset_size: 143713 --- # Dataset Card for "MULTI_VALUE_stsb_regularized_plurals" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Har11k/demotrain1

--- license: apache-2.0 task_categories: - tabular-classification language: - en ---

CyberHarem/craven_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of craven/クレイヴン/克雷文 (Azur Lane) This is the dataset of craven/クレイヴン/克雷文 (Azur Lane), containing 17 images and their tags. The core tags of this character are `long_hair, purple_hair, drill_hair, yellow_eyes, bangs, breasts, 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 | 17 | 14.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 17 | 10.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 36 | 20.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 17 | 13.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 36 | 25.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/craven_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/craven_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 17 | ![](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, smile, looking_at_viewer, solo, open_mouth, white_thighhighs, navel, pleated_skirt, full_body, sailor_collar, school_uniform, shirt, shoes, standing, cheerleader, collarbone, long_sleeves, midriff, one_eye_closed, pom_pom_(cheerleading), white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | smile | looking_at_viewer | solo | open_mouth | white_thighhighs | navel | pleated_skirt | full_body | sailor_collar | school_uniform | shirt | shoes | standing | cheerleader | collarbone | long_sleeves | midriff | one_eye_closed | pom_pom_(cheerleading) | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------|:--------------------|:-------|:-------------|:-------------------|:--------|:----------------|:------------|:----------------|:-----------------|:--------|:--------|:-----------|:--------------|:-------------|:---------------|:----------|:-----------------|:-------------------------|:-------------------| | 0 | 17 | ![](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 |

ramixpe/sp_llama_format

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 20479775 num_examples: 20537 download_size: 4297673 dataset_size: 20479775 configs: - config_name: default data_files: - split: train path: data/train-* ---

Zakay/kuririn

--- license: openrail ---

autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot-mathema-f8e841-1882064209

--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_cot eval_info: task: text_zero_shot_classification model: ArthurZ/opt-350m metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_cot dataset_config: mathemakitten--winobias_antistereotype_test_cot dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: ArthurZ/opt-350m * Dataset: mathemakitten/winobias_antistereotype_test_cot * Config: mathemakitten--winobias_antistereotype_test_cot * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.

EleutherAI/lambada_openai

--- pretty_name: LAMBADA OpenAI language_creators: - machine-generated license: mit multilinguality: - translation task_ids: - language-modeling source_datasets: - lambada size_categories: - 1K<n<10K language: - de - en - es - fr - it dataset_info: - config_name: default features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: de features: - name: text dtype: string splits: - name: test num_bytes: 1904576 num_examples: 5153 download_size: 1985231 dataset_size: 1904576 - config_name: en features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: es features: - name: text dtype: string splits: - name: test num_bytes: 1821735 num_examples: 5153 download_size: 1902349 dataset_size: 1821735 - config_name: fr features: - name: text dtype: string splits: - name: test num_bytes: 1948795 num_examples: 5153 download_size: 2028703 dataset_size: 1948795 - config_name: it features: - name: text dtype: string splits: - name: test num_bytes: 1813420 num_examples: 5153 download_size: 1894613 dataset_size: 1813420 --- ## Dataset Description - **Repository:** [openai/gpt2](https://github.com/openai/gpt-2) - **Paper:** Radford et al. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) ### Dataset Summary This dataset is comprised of the LAMBADA test split as pre-processed by OpenAI (see relevant discussions [here](https://github.com/openai/gpt-2/issues/131#issuecomment-497136199) and [here](https://github.com/huggingface/transformers/issues/491)). It also contains machine translated versions of the split in German, Spanish, French, and Italian. LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole text, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. ### Languages English, German, Spanish, French, and Italian. ### Source Data For non-English languages, the data splits were produced by Google Translate. See the [`translation_script.py`](translation_script.py) for more details. ## Additional Information ### Hash Checksums For data integrity checks we leave the following checksums for the files in this dataset: | File Name | Checksum (SHA-256) | |--------------------------------------------------------------------------|------------------------------------------------------------------| | lambada_test_de.jsonl | 51c6c1795894c46e88e4c104b5667f488efe79081fb34d746b82b8caa663865e | | [openai/lambada_test.jsonl](https://openaipublic.blob.core.windows.net/gpt-2/data/lambada_test.jsonl) | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_en.jsonl | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_es.jsonl | ffd760026c647fb43c67ce1bc56fd527937304b348712dce33190ea6caba6f9c | | lambada_test_fr.jsonl | 941ec6a73dba7dc91c860bf493eb66a527cd430148827a4753a4535a046bf362 | | lambada_test_it.jsonl | 86654237716702ab74f42855ae5a78455c1b0e50054a4593fb9c6fcf7fad0850 | ### Licensing License: [Modified MIT](https://github.com/openai/gpt-2/blob/master/LICENSE) ### Citation ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` ```bibtex @misc{ author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel}, title={The LAMBADA dataset}, DOI={10.5281/zenodo.2630551}, publisher={Zenodo}, year={2016}, month={Aug} } ``` ### Contributions Thanks to Sid Black ([@sdtblck](https://github.com/sdtblck)) for translating the `lambada_openai` dataset into the non-English languages. Thanks to Jonathan Tow ([@jon-tow](https://github.com/jon-tow)) for adding this dataset.

mmenendezg/pneumonia_x_ray

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': normal '1': pneumonia splits: - name: train num_bytes: 126906525.958 num_examples: 4187 - name: validation num_bytes: 27684376.78 num_examples: 1045 - name: test num_bytes: 16275405.0 num_examples: 624 download_size: 153423742 dataset_size: 170866307.738 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Chest X-Ray Pneumonia Dataset This dataset contains chest x-ray images of independent patients that can be classified into `normal` (healthy) or `pneumonia` (diseased) patients. This dataset is a processed version of the original `Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images` dataset provided by the *University of California San Diego*. The dataset contains three splits: - **Train**: 4187 images - **Validation**: 1045 images - **Test**: 624 images The shape of the images is `[500, 500, 3]`, and the labels have two possible values: - 0: **Normal** - 1: **Pneumonia** >**References**: > > - Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images”, Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3

artixjain/dif_instruct_training

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 56676 num_examples: 332 download_size: 17252 dataset_size: 56676 configs: - config_name: default data_files: - split: train path: data/train-* ---

ElMerOs/Prueba

--- license: openrail ---

chathuranga-jayanath/selfapr-manipulation-bug-error-context-10000

--- dataset_info: features: - name: fix dtype: string - name: ctx dtype: string splits: - name: train num_bytes: 5017924 num_examples: 8000 - name: validation num_bytes: 614517 num_examples: 1000 - name: test num_bytes: 608165 num_examples: 1000 download_size: 2850672 dataset_size: 6240606 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

markr23/processed_reddit_dataset

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: special_tokens_mask sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 127904400.0 num_examples: 35529 download_size: 35925794 dataset_size: 127904400.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Quirina/common_voice_13_0_nl_pseudo_labelled

--- dataset_info: config_name: nl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: validation num_bytes: 356439925.37 num_examples: 10930 download_size: 352292365 dataset_size: 356439925.37 configs: - config_name: nl data_files: - split: validation path: nl/validation-* ---

yainage90/fashion-pattern-images

--- license: mit task_categories: - zero-shot-classification language: - en tags: - fashion - clothes - pattern size_categories: - 10K<n<100K --- This dataset consists of 10,898 images, which are composed of 19 different patterns. The data was collected in a manner that ensures maximum balance across each pattern. Minimum is 552(argyle) and maximum is 634(zebra). ## 1. Patterns ``` 1. argyle 2. camouflage(military) 3. checked 4. dot 5. floral 6. geometric 7. gradient(gradation) 8. graphic 9. houndstooth 10. leopard 11. lettering 12. muji 13. paisley 14. snake skin 15. snow flake 16. stripe 17. tropical 18. zebra 19. zigzag ``` ## 2. Product categories in images Tried to include as wide a variety of product categories as possible to prevent the model biased. These categories include outerwear, tops, bottoms, hats, shoes, underwear, scarves, ties, socks, phone cases, and so on. ## 3. Data source The sources of this data are Musinsa, SSF, Amazon, eBay, Pinterest, and Google Image Search.

CyberHarem/choco_neuralcloud

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of choco/チョコ/巧可 (Neural Cloud) This is the dataset of choco/チョコ/巧可 (Neural Cloud), containing 96 images and their tags. The core tags of this character are `hair_ornament, long_hair, blue_eyes, breasts, hat, braid, blonde_hair, bangs, beret, medium_breasts, single_braid`, 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 | 96 | 126.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 96 | 74.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 239 | 162.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 96 | 112.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 239 | 225.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/choco_neuralcloud/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/choco_neuralcloud', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](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, arm_warmers, blush, solo, looking_at_viewer, assault_rifle, chocolate_bar, food_in_mouth, white_shirt, brown_hair, holding_gun, layered_skirt, simple_background, belt_pouch, boots, brown_headwear, buttons, hair_ribbon, short_sleeves, white_background, brown_skirt, full_body, hair_between_eyes, mouth_hold | | 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, gloves, solo, headset, looking_at_viewer, military_uniform, blush, camouflage, load_bearing_vest, long_sleeves, scarf, bandana, hair_between_eyes, hair_bow, helmet, holding_gun, jacket, m4_carbine, smile | | 2 | 8 | ![](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, spread_legs, nipples, 1boy, cum_in_pussy, hetero, solo_focus, tears, bar_censor, korean_text, navel, nude, open_mouth, penis, after_sex, brown_hair, heart, on_back, vaginal | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | arm_warmers | blush | solo | looking_at_viewer | assault_rifle | chocolate_bar | food_in_mouth | white_shirt | brown_hair | holding_gun | layered_skirt | simple_background | belt_pouch | boots | brown_headwear | buttons | hair_ribbon | short_sleeves | white_background | brown_skirt | full_body | hair_between_eyes | mouth_hold | gloves | headset | military_uniform | camouflage | load_bearing_vest | long_sleeves | scarf | bandana | hair_bow | helmet | jacket | m4_carbine | smile | spread_legs | nipples | 1boy | cum_in_pussy | hetero | solo_focus | tears | bar_censor | korean_text | navel | nude | open_mouth | penis | after_sex | heart | on_back | vaginal | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:--------|:-------|:--------------------|:----------------|:----------------|:----------------|:--------------|:-------------|:--------------|:----------------|:--------------------|:-------------|:--------|:-----------------|:----------|:--------------|:----------------|:-------------------|:--------------|:------------|:--------------------|:-------------|:---------|:----------|:-------------------|:-------------|:--------------------|:---------------|:--------|:----------|:-----------|:---------|:---------|:-------------|:--------|:--------------|:----------|:-------|:---------------|:---------|:-------------|:--------|:-------------|:--------------|:--------|:-------|:-------------|:--------|:------------|:--------|:----------|:----------| | 0 | 13 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](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 |

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/659a46ba

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1318 dataset_size: 178 --- # Dataset Card for "659a46ba" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

cstr/dpo-mix-7k-simplified-de

--- license: mit language: - de --- this is going to be an experimental (poor) German Mixtral translation of alvarobartt/dpo-mix-7k-simplified, only for testing purposes

argilla/alpaca_data_cleaned

--- dataset_info: features: - name: text dtype: 'null' - name: inputs struct: - name: _instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prediction dtype: 'null' - name: prediction_agent dtype: 'null' - name: annotation dtype: string - name: annotation_agent dtype: string - name: vectors struct: - name: input sequence: float64 - name: instruction sequence: float64 - name: output 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: 975104502 num_examples: 51713 download_size: 679574648 dataset_size: 975104502 --- # Dataset Card for "alpaca_data_cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Devio__testC

--- pretty_name: Evaluation run of Devio/testC dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Devio/testC](https://huggingface.co/Devio/testC) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Devio__testC\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-02T17:27:16.860385](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__testC/blob/main/results_2023-09-02T17%3A27%3A16.860385.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\": 0.28185588236286707,\n\ \ \"acc_stderr\": 0.03225753349873974,\n \"acc_norm\": 0.2855290591736718,\n\ \ \"acc_norm_stderr\": 0.03226027924923892,\n \"mc1\": 0.20318237454100369,\n\ \ \"mc1_stderr\": 0.014085666526340882,\n \"mc2\": 0.35665813452391837,\n\ \ \"mc2_stderr\": 0.014271431688144938\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.35494880546075086,\n \"acc_stderr\": 0.013983036904094097,\n\ \ \"acc_norm\": 0.39590443686006827,\n \"acc_norm_stderr\": 0.014291228393536583\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4529974108743278,\n\ \ \"acc_stderr\": 0.004967685204073108,\n \"acc_norm\": 0.6287592113124876,\n\ \ \"acc_norm_stderr\": 0.004821492994082116\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.22962962962962963,\n\ \ \"acc_stderr\": 0.03633384414073461,\n \"acc_norm\": 0.22962962962962963,\n\ \ \"acc_norm_stderr\": 0.03633384414073461\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.32894736842105265,\n \"acc_stderr\": 0.03823428969926603,\n\ \ \"acc_norm\": 0.32894736842105265,\n \"acc_norm_stderr\": 0.03823428969926603\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n\ \ \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n \ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2981132075471698,\n \"acc_stderr\": 0.028152837942493857,\n\ \ \"acc_norm\": 0.2981132075471698,\n \"acc_norm_stderr\": 0.028152837942493857\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\"\ : 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n\ \ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.27167630057803466,\n\ \ \"acc_stderr\": 0.0339175032232166,\n \"acc_norm\": 0.27167630057803466,\n\ \ \"acc_norm_stderr\": 0.0339175032232166\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.16,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.16,\n\ \ \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.28936170212765955,\n \"acc_stderr\": 0.02964400657700962,\n\ \ \"acc_norm\": 0.28936170212765955,\n \"acc_norm_stderr\": 0.02964400657700962\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2827586206896552,\n \"acc_stderr\": 0.037528339580033376,\n\ \ \"acc_norm\": 0.2827586206896552,\n \"acc_norm_stderr\": 0.037528339580033376\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.23809523809523808,\n \"acc_stderr\": 0.021935878081184756,\n \"\ acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.021935878081184756\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3258064516129032,\n \"acc_stderr\": 0.0266620105785671,\n \"acc_norm\"\ : 0.3258064516129032,\n \"acc_norm_stderr\": 0.0266620105785671\n },\n\ \ \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.0317852971064275,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.0317852971064275\n },\n \"harness|hendrycksTest-high_school_computer_science|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n \ \ },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \ \ \"acc\": 0.23030303030303031,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.23030303030303031,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.35858585858585856,\n \"acc_stderr\": 0.03416903640391521,\n \"\ acc_norm\": 0.35858585858585856,\n \"acc_norm_stderr\": 0.03416903640391521\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.36787564766839376,\n \"acc_stderr\": 0.03480175668466036,\n\ \ \"acc_norm\": 0.36787564766839376,\n \"acc_norm_stderr\": 0.03480175668466036\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.34615384615384615,\n \"acc_stderr\": 0.024121125416941183,\n\ \ \"acc_norm\": 0.34615384615384615,\n \"acc_norm_stderr\": 0.024121125416941183\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712177,\n \ \ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712177\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.33613445378151263,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.33613445378151263,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.3431192660550459,\n \"acc_stderr\": 0.02035477773608604,\n \"\ acc_norm\": 0.3431192660550459,\n \"acc_norm_stderr\": 0.02035477773608604\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4351851851851852,\n \"acc_stderr\": 0.033812000056435254,\n \"\ acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.033812000056435254\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.20675105485232068,\n \"acc_stderr\": 0.0263616516683891,\n \ \ \"acc_norm\": 0.20675105485232068,\n \"acc_norm_stderr\": 0.0263616516683891\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.15695067264573992,\n\ \ \"acc_stderr\": 0.024413587174907412,\n \"acc_norm\": 0.15695067264573992,\n\ \ \"acc_norm_stderr\": 0.024413587174907412\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2824427480916031,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.2824427480916031,\n \"acc_norm_stderr\": 0.03948406125768361\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.14049586776859505,\n \"acc_stderr\": 0.03172233426002161,\n \"\ acc_norm\": 0.14049586776859505,\n \"acc_norm_stderr\": 0.03172233426002161\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.23148148148148148,\n\ \ \"acc_stderr\": 0.04077494709252628,\n \"acc_norm\": 0.23148148148148148,\n\ \ \"acc_norm_stderr\": 0.04077494709252628\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.20535714285714285,\n\ \ \"acc_stderr\": 0.038342410214190735,\n \"acc_norm\": 0.20535714285714285,\n\ \ \"acc_norm_stderr\": 0.038342410214190735\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.4174757281553398,\n \"acc_stderr\": 0.04882840548212237,\n\ \ \"acc_norm\": 0.4174757281553398,\n \"acc_norm_stderr\": 0.04882840548212237\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.18803418803418803,\n\ \ \"acc_stderr\": 0.025598193686652244,\n \"acc_norm\": 0.18803418803418803,\n\ \ \"acc_norm_stderr\": 0.025598193686652244\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.210727969348659,\n\ \ \"acc_stderr\": 0.014583812465862553,\n \"acc_norm\": 0.210727969348659,\n\ \ \"acc_norm_stderr\": 0.014583812465862553\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.22832369942196531,\n \"acc_stderr\": 0.02259870380432162,\n\ \ \"acc_norm\": 0.22832369942196531,\n \"acc_norm_stderr\": 0.02259870380432162\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.3006535947712418,\n \"acc_stderr\": 0.02625605383571896,\n\ \ \"acc_norm\": 0.3006535947712418,\n \"acc_norm_stderr\": 0.02625605383571896\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.26688102893890675,\n\ \ \"acc_stderr\": 0.02512263760881664,\n \"acc_norm\": 0.26688102893890675,\n\ \ \"acc_norm_stderr\": 0.02512263760881664\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.25308641975308643,\n \"acc_stderr\": 0.02419180860071301,\n\ \ \"acc_norm\": 0.25308641975308643,\n \"acc_norm_stderr\": 0.02419180860071301\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2624113475177305,\n \"acc_stderr\": 0.026244920349843003,\n \ \ \"acc_norm\": 0.2624113475177305,\n \"acc_norm_stderr\": 0.026244920349843003\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142695,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142695\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.22058823529411764,\n \"acc_stderr\": 0.01677467236546851,\n \ \ \"acc_norm\": 0.22058823529411764,\n \"acc_norm_stderr\": 0.01677467236546851\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2727272727272727,\n\ \ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.2727272727272727,\n\ \ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.39591836734693875,\n \"acc_stderr\": 0.03130802899065686,\n\ \ \"acc_norm\": 0.39591836734693875,\n \"acc_norm_stderr\": 0.03130802899065686\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.263681592039801,\n\ \ \"acc_stderr\": 0.03115715086935556,\n \"acc_norm\": 0.263681592039801,\n\ \ \"acc_norm_stderr\": 0.03115715086935556\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.21084337349397592,\n\ \ \"acc_stderr\": 0.0317555478662992,\n \"acc_norm\": 0.21084337349397592,\n\ \ \"acc_norm_stderr\": 0.0317555478662992\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.14619883040935672,\n \"acc_stderr\": 0.027097290118070803,\n\ \ \"acc_norm\": 0.14619883040935672,\n \"acc_norm_stderr\": 0.027097290118070803\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.20318237454100369,\n\ \ \"mc1_stderr\": 0.014085666526340882,\n \"mc2\": 0.35665813452391837,\n\ \ \"mc2_stderr\": 0.014271431688144938\n }\n}\n```" repo_url: https://huggingface.co/Devio/testC leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|arc:challenge|25_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hellaswag|10_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-02T17:27:16.860385.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-management|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T17:27:16.860385.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_02T17_27_16.860385 path: - '**/details_harness|truthfulqa:mc|0_2023-09-02T17:27:16.860385.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-02T17:27:16.860385.parquet' - config_name: results data_files: - split: 2023_09_02T17_27_16.860385 path: - results_2023-09-02T17:27:16.860385.parquet - split: latest path: - results_2023-09-02T17:27:16.860385.parquet --- # Dataset Card for Evaluation run of Devio/testC ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Devio/testC - **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 [Devio/testC](https://huggingface.co/Devio/testC) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Devio__testC", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-02T17:27:16.860385](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__testC/blob/main/results_2023-09-02T17%3A27%3A16.860385.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": 0.28185588236286707, "acc_stderr": 0.03225753349873974, "acc_norm": 0.2855290591736718, "acc_norm_stderr": 0.03226027924923892, "mc1": 0.20318237454100369, "mc1_stderr": 0.014085666526340882, "mc2": 0.35665813452391837, "mc2_stderr": 0.014271431688144938 }, "harness|arc:challenge|25": { "acc": 0.35494880546075086, "acc_stderr": 0.013983036904094097, "acc_norm": 0.39590443686006827, "acc_norm_stderr": 0.014291228393536583 }, "harness|hellaswag|10": { "acc": 0.4529974108743278, "acc_stderr": 0.004967685204073108, "acc_norm": 0.6287592113124876, "acc_norm_stderr": 0.004821492994082116 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.22962962962962963, "acc_stderr": 0.03633384414073461, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.03633384414073461 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.32894736842105265, "acc_stderr": 0.03823428969926603, "acc_norm": 0.32894736842105265, "acc_norm_stderr": 0.03823428969926603 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.21, "acc_stderr": 0.04093601807403326, "acc_norm": 0.21, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2981132075471698, "acc_stderr": 0.028152837942493857, "acc_norm": 0.2981132075471698, "acc_norm_stderr": 0.028152837942493857 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.27167630057803466, "acc_stderr": 0.0339175032232166, "acc_norm": 0.27167630057803466, "acc_norm_stderr": 0.0339175032232166 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077636, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077636 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.16, "acc_stderr": 0.03684529491774708, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.28936170212765955, "acc_stderr": 0.02964400657700962, "acc_norm": 0.28936170212765955, "acc_norm_stderr": 0.02964400657700962 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813344, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813344 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2827586206896552, "acc_stderr": 0.037528339580033376, "acc_norm": 0.2827586206896552, "acc_norm_stderr": 0.037528339580033376 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.23809523809523808, "acc_stderr": 0.021935878081184756, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.021935878081184756 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3258064516129032, "acc_stderr": 0.0266620105785671, "acc_norm": 0.3258064516129032, "acc_norm_stderr": 0.0266620105785671 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0317852971064275, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0317852971064275 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.23030303030303031, "acc_stderr": 0.0328766675860349, "acc_norm": 0.23030303030303031, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.35858585858585856, "acc_stderr": 0.03416903640391521, "acc_norm": 0.35858585858585856, "acc_norm_stderr": 0.03416903640391521 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.36787564766839376, "acc_stderr": 0.03480175668466036, "acc_norm": 0.36787564766839376, "acc_norm_stderr": 0.03480175668466036 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.34615384615384615, "acc_stderr": 0.024121125416941183, "acc_norm": 0.34615384615384615, "acc_norm_stderr": 0.024121125416941183 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25925925925925924, "acc_stderr": 0.026719240783712177, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.026719240783712177 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.33613445378151263, "acc_stderr": 0.030684737115135356, "acc_norm": 0.33613445378151263, "acc_norm_stderr": 0.030684737115135356 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3431192660550459, "acc_stderr": 0.02035477773608604, "acc_norm": 0.3431192660550459, "acc_norm_stderr": 0.02035477773608604 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4351851851851852, "acc_stderr": 0.033812000056435254, "acc_norm": 0.4351851851851852, "acc_norm_stderr": 0.033812000056435254 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604246, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604246 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.20675105485232068, "acc_stderr": 0.0263616516683891, "acc_norm": 0.20675105485232068, "acc_norm_stderr": 0.0263616516683891 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.15695067264573992, "acc_stderr": 0.024413587174907412, "acc_norm": 0.15695067264573992, "acc_norm_stderr": 0.024413587174907412 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2824427480916031, "acc_stderr": 0.03948406125768361, "acc_norm": 0.2824427480916031, "acc_norm_stderr": 0.03948406125768361 }, "harness|hendrycksTest-international_law|5": { "acc": 0.14049586776859505, "acc_stderr": 0.03172233426002161, "acc_norm": 0.14049586776859505, "acc_norm_stderr": 0.03172233426002161 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.23148148148148148, "acc_stderr": 0.04077494709252628, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.04077494709252628 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.20535714285714285, "acc_stderr": 0.038342410214190735, "acc_norm": 0.20535714285714285, "acc_norm_stderr": 0.038342410214190735 }, "harness|hendrycksTest-management|5": { "acc": 0.4174757281553398, "acc_stderr": 0.04882840548212237, "acc_norm": 0.4174757281553398, "acc_norm_stderr": 0.04882840548212237 }, "harness|hendrycksTest-marketing|5": { "acc": 0.18803418803418803, "acc_stderr": 0.025598193686652244, "acc_norm": 0.18803418803418803, "acc_norm_stderr": 0.025598193686652244 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.210727969348659, "acc_stderr": 0.014583812465862553, "acc_norm": 0.210727969348659, "acc_norm_stderr": 0.014583812465862553 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.22832369942196531, "acc_stderr": 0.02259870380432162, "acc_norm": 0.22832369942196531, "acc_norm_stderr": 0.02259870380432162 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.3006535947712418, "acc_stderr": 0.02625605383571896, "acc_norm": 0.3006535947712418, "acc_norm_stderr": 0.02625605383571896 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.26688102893890675, "acc_stderr": 0.02512263760881664, "acc_norm": 0.26688102893890675, "acc_norm_stderr": 0.02512263760881664 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.25308641975308643, "acc_stderr": 0.02419180860071301, "acc_norm": 0.25308641975308643, "acc_norm_stderr": 0.02419180860071301 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843003, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843003 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142695, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142695 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.22058823529411764, "acc_stderr": 0.01677467236546851, "acc_norm": 0.22058823529411764, "acc_norm_stderr": 0.01677467236546851 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2727272727272727, "acc_stderr": 0.04265792110940589, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.04265792110940589 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.39591836734693875, "acc_stderr": 0.03130802899065686, "acc_norm": 0.39591836734693875, "acc_norm_stderr": 0.03130802899065686 }, "harness|hendrycksTest-sociology|5": { "acc": 0.263681592039801, "acc_stderr": 0.03115715086935556, "acc_norm": 0.263681592039801, "acc_norm_stderr": 0.03115715086935556 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-virology|5": { "acc": 0.21084337349397592, "acc_stderr": 0.0317555478662992, "acc_norm": 0.21084337349397592, "acc_norm_stderr": 0.0317555478662992 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.14619883040935672, "acc_stderr": 0.027097290118070803, "acc_norm": 0.14619883040935672, "acc_norm_stderr": 0.027097290118070803 }, "harness|truthfulqa:mc|0": { "mc1": 0.20318237454100369, "mc1_stderr": 0.014085666526340882, "mc2": 0.35665813452391837, "mc2_stderr": 0.014271431688144938 } } ``` ### 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]

Besteasy/CG-Eval

--- task_categories: - text-generation language: - zh pretty_name: CG-Eval size_categories: - 1M<n<10M license: cc-by-sa-4.0 --- ## 评测数据集简介 CG-Eval是甲骨易AI研究院与LanguageX AI Lab联合研发的针对中文大模型生成能力的测试基准。在此项测试中，受测的中文大语言模型需要对科技与工程、人文与社会科学、数学计算、医师资格考试、司法考试、注册会计师考试这六个大科目类别下的55个子科目的11000道不同类型问题做出准确且相关的回答。我们设计了一套复合的打分系统，对于非计算题，每一道名词解释题和简答题都有标准参考答案，采用多个标准打分然后加权求和。对于计算题目，我们会提取最终计算结果和解题过程，然后综合打分。数据集包括以下字段大科目类别,子科目名称,题目类型, 题目编号,题目文本,题目答案的汉字长度,题目prompt ## 论文及数据集下载 CG-Eval论文 https://arxiv.org/abs/2308.04823<br> CG-Eval测试数据集下载地址 https://huggingface.co/datasets/Besteasy/CG-Eval<br> CG-Eval自动化评测地址 http://cgeval.besteasy.com/<br> ## 评测方法下载数据集后，请使用“题目prompt”列对应的提示词向模型提问，并在csv文件中增加“回答”列，存放模型的回复。请注意题目的回答要与提示词、问题编号、科目名称对应。在收集到所有回答后，请将csv文件提交到评测网站 http://cgeval.besteasy.com/ 您需要提交的csv文件应具有以下字段：大科目类别,子科目名称,题目类型, 题目编号,题目文本,题目答案的汉字长度,题目prompt,回答网站会自动计算分数，您可以选择是否将分数同步到排行榜。 ## Citation If you find the code and testset are useful in your research, please consider citing ``` @misc{zeng2023evaluating, title={Evaluating the Generation Capabilities of Large Chinese Language Models}, author={Hui Zeng and Jingyuan Xue and Meng Hao and Chen Sun and Bin Ning and Na Zhang}, year={2023}, eprint={2308.04823}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License The CG-Eval dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).

liuyanchen1015/MULTI_VALUE_qqp_perfect_already

--- 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: 237907 num_examples: 1217 - name: test num_bytes: 2232355 num_examples: 11275 - name: train num_bytes: 2113428 num_examples: 10705 download_size: 2700603 dataset_size: 4583690 --- # Dataset Card for "MULTI_VALUE_qqp_perfect_already" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

UCLA-AGI/SPIN_iter2

--- license: apache-2.0 dataset_info: features: - name: generated list: - name: content dtype: string - name: role dtype: string - name: real list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 215908120 num_examples: 49792 - name: test num_bytes: 2165130 num_examples: 500 download_size: 122061358 dataset_size: 218073250 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

xianning/test

--- license: apache-2.0 language: - en configs: - config_name: asqa data_files: - split: gpt_3.5_turbo_instruct path: "asqa/gpt-3.5-turbo-instruct_result_dataset.jsonl" - config_name: asqa_original_resopnse data_files: - split: gpt_3.5_turbo_instruct path: "asqa_original_response/gpt-3.5-turbo-instruct.jsonl" ---

one-sec-cv12/chunk_9

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 20343446640.375 num_examples: 211805 download_size: 18016130134 dataset_size: 20343446640.375 --- # Dataset Card for "chunk_9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pisterlabs/promptset

--- license: mit dataset_info: features: - name: date_collected dtype: string - name: repo_name dtype: string - name: file_name dtype: string - name: file_contents dtype: string - name: prompts sequence: string splits: - name: train num_bytes: 712546975 num_examples: 93142 download_size: 248931003 dataset_size: 712546975 configs: - config_name: default data_files: - split: train path: data/train-* ---

luckychao/Chat-Models-Backdoor-Attacking

irds/lotte_recreation_test_forum

--- pretty_name: '`lotte/recreation/test/forum`' viewer: false source_datasets: ['irds/lotte_recreation_test'] task_categories: - text-retrieval --- # Dataset Card for `lotte/recreation/test/forum` The `lotte/recreation/test/forum` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/recreation/test/forum). # Data This dataset provides: - `queries` (i.e., topics); count=2,002 - `qrels`: (relevance assessments); count=6,947 - For `docs`, use [`irds/lotte_recreation_test`](https://huggingface.co/datasets/irds/lotte_recreation_test) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/lotte_recreation_test_forum', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/lotte_recreation_test_forum', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```

subset-data/finetune-data-1215cfd29a6d

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 439213.3333333333 num_examples: 56 - name: test num_bytes: 31372.380952380954 num_examples: 4 - name: valid num_bytes: 23529.285714285714 num_examples: 3 download_size: 161148 dataset_size: 494115.0 --- # Dataset Card for "finetune-data-1215cfd29a6d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kopyl/mapped-833-pokemon-sdxl-1024

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: prompt_embeds sequence: sequence: float32 - name: pooled_prompt_embeds sequence: float32 - name: model_input sequence: sequence: sequence: float32 splits: - name: train num_bytes: 869477161.0 num_examples: 833 download_size: 851613359 dataset_size: 869477161.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

orpo-explorers/OpenHermesPreferences-500k

--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidates_completions sequence: string - name: candidate_policies sequence: string - name: ranks sequence: int64 - name: rank_str dtype: string - name: chosen_policy dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected_policy dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 3670117618.1669345 num_examples: 500000 download_size: 1830624597 dataset_size: 3670117618.1669345 configs: - config_name: default data_files: - split: train path: data/train-* ---

irds/wikir_ens78k

--- pretty_name: '`wikir/ens78k`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikir/ens78k` The `wikir/ens78k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/ens78k). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=2,456,637 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikir_ens78k', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` 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{Frej2020Wikir, title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={LREC}, year={2020} } @inproceedings{Frej2020MlWikir, title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={CIRCLE}, year={2020} } ```

Ga88/Clovis5

--- license: openrail ---

MapleWish/LUNA16_subsets

--- license: cc ---

p1atdev/niji-v5

--- license: cc0-1.0 --- 私がnijijourney v5で生成した画像。自由に使えます。(けど詐欺とか犯罪につかうのはやめてね) おすすめの使い方としては、とりあえず中の画像を見てみて好きなものだけ選んで使うとよいと思います。全体の注意点として、必ずしもすべての画像にキャプションが付属してるとは限らないのと、キャプションがついていてもそのまま使うと問題が発生する可能性があるのであまり信用しないでください。また人為的なミスにより、4分割されずに結合している画像があったり、過度に分割されている画像があったりするので注意してください。 ## vol1 だいたい2000枚くらいで、多分全部デフォルトスタイルのものです。解答すると中にLAION Aesthetic v2のスコアでいくつかのフォルダに分類されています。`aesthetic_50` ならスコア0.5以上のものです。`not_aesthetic` は0.5未満のものです。ただし、`exceptional` フォルダはチェリーピックした画像が入っており、`aesthetic_xx` の中のものと重複します。`exclude` フォルダは、主観で奇妙なものを除いたものです。 `aesthetic_xx` と `exceptional` にはキャプション(BLIP2、Tagger)ファイルがついていますが、いろいろ変な調整しているのでおそらく各自でキャプションをつけ直したほうがいいと思います。 ## vol2 だいたい1200枚くらいで、複数のスタイルモードで生成したものが含まれます。手動でスタイルごとにフォルダを分けています。 `default`、`cute`、`expressive`、`scenic` はそれぞれのスタイルっぽい画像で分類していますが、たまに分類を間違えています(ごめん)。 `clear` と `rough painting` は、個人的に近いと思ったスタイルの画像を入れていて、4つのスタイルの画像とは重複しません。 |default|cute|expressive|scenic|clear|rough painting| |-|-|-|-|-|-| |<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/ROBUltJHEdadypi8JJ7QZ.jpeg" width="200px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/lPpxxFZggOD4QZLgQ03WS.jpeg" width="200px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/E5T2nAxwiYxSORoGov_8e.jpeg" width="200px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/juur651e8PS1TcDVwxITm.jpeg" width="200px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/j9GUce5nsKMVN4z2E14sW.jpeg" width="300px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/w3OrlxnDtFEiDEUe8ey5c.jpeg" width="300px" />| ## vol3 450枚くらいです。キャプションは一切ついていません。雰囲気で分類しています。 - `background`: 背景のみで人物が写っていないもの - `comic`: 白黒だったり漫画風なもの(百合が多いので微妙に注意) - `ink painting`: 水墨画・水彩っぽいもの - `scenic`: scenic っぽい画像で、人物が写っているものも含む。`background` の画像と一部重複する。含まれる画像の例 |background|comic|ink painting|scenic| |-|-|-|-| |<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/ZoH3PCg918_WhoMfKu-JJ.png" width="300px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/i5KLwkPJN0guLgval5aBr.png" width="200px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/MrGOEretLVNjM4ZaO8yPe.png" width="200px" />|<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/uHH7rswou_9ZzL1-phbip.png" width="200px" />| ## vol4 現在は48枚です。 - `minimalist`: 非常にシンプルな感じの画風の画像含まれる画像の例 |minimalist| |-| |<img src="https://s3.amazonaws.com/moonup/production/uploads/6305db1fcfbde33ef7d480ff/UCuGYVyvkqS7JmUseKF3c.png" width="200px" />|

thestattrak/kevin

--- license: openrail ---

DmitrMakeev/Deforum-file

--- license: openrail ---

CyberHarem/narumiya_yume_idolmastercinderellagirls

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of narumiya_yume/成宮由愛 (THE iDOLM@STER: Cinderella Girls) This is the dataset of narumiya_yume/成宮由愛 (THE iDOLM@STER: Cinderella Girls), containing 125 images and their tags. The core tags of this character are `grey_hair, short_hair, mole, mole_under_eye, brown_eyes, bangs, hairband, hair_between_eyes, bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 125 | 99.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 125 | 76.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 256 | 143.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 125 | 94.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 256 | 171.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/narumiya_yume_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/narumiya_yume_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 | 11 | ![](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, solo, :d, looking_at_viewer, open_mouth, white_background, simple_background, dress, long_sleeves, hair_bow, shirt, upper_body | | 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, hair_flower, solo, smile, bracelet, dress, looking_at_viewer, sitting | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | solo | :d | looking_at_viewer | open_mouth | white_background | simple_background | dress | long_sleeves | hair_bow | shirt | upper_body | hair_flower | smile | bracelet | sitting | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-----|:--------------------|:-------------|:-------------------|:--------------------|:--------|:---------------|:-----------|:--------|:-------------|:--------------|:--------|:-----------|:----------| | 0 | 11 | ![](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 | | | | | | 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 |

open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model

--- pretty_name: Evaluation run of TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model](https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T09:16:00.873424](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model/blob/main/results_2023-10-29T09-16-00.873424.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.29247063758389263,\n\ \ \"em_stderr\": 0.004658574242541351,\n \"f1\": 0.34158871644295397,\n\ \ \"f1_stderr\": 0.004610159225684241,\n \"acc\": 0.40783419789572956,\n\ \ \"acc_stderr\": 0.009578253696730769\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.29247063758389263,\n \"em_stderr\": 0.004658574242541351,\n\ \ \"f1\": 0.34158871644295397,\n \"f1_stderr\": 0.004610159225684241\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06823351023502654,\n \ \ \"acc_stderr\": 0.006945358944067431\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n\ \ }\n}\n```" repo_url: https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|arc:challenge|25_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-24T06:39:02.499923.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T09_16_00.873424 path: - '**/details_harness|drop|3_2023-10-29T09-16-00.873424.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T09-16-00.873424.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T09_16_00.873424 path: - '**/details_harness|gsm8k|5_2023-10-29T09-16-00.873424.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T09-16-00.873424.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hellaswag|10_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_24T06_39_02.499923 path: - '**/details_harness|truthfulqa:mc|0_2023-08-24T06:39:02.499923.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-24T06:39:02.499923.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T09_16_00.873424 path: - '**/details_harness|winogrande|5_2023-10-29T09-16-00.873424.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T09-16-00.873424.parquet' - config_name: results data_files: - split: 2023_10_29T09_16_00.873424 path: - results_2023-10-29T09-16-00.873424.parquet - split: latest path: - results_2023-10-29T09-16-00.873424.parquet --- # Dataset Card for Evaluation run of TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model - **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 [TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model](https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T09:16:00.873424](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model/blob/main/results_2023-10-29T09-16-00.873424.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.29247063758389263, "em_stderr": 0.004658574242541351, "f1": 0.34158871644295397, "f1_stderr": 0.004610159225684241, "acc": 0.40783419789572956, "acc_stderr": 0.009578253696730769 }, "harness|drop|3": { "em": 0.29247063758389263, "em_stderr": 0.004658574242541351, "f1": 0.34158871644295397, "f1_stderr": 0.004610159225684241 }, "harness|gsm8k|5": { "acc": 0.06823351023502654, "acc_stderr": 0.006945358944067431 }, "harness|winogrande|5": { "acc": 0.7474348855564326, "acc_stderr": 0.012211148449394105 } } ``` ### 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]

arthurmluz/GPTextSum2_data-wiki_cstnews_results

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 92922 num_examples: 20 download_size: 89357 dataset_size: 92922 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "GPTextSum2_data-wiki_cstnews_results" rouge= {'rouge1': 0.40559145209215386, 'rouge2': 0.1858323707445477, 'rougeL': 0.2713738809702273, 'rougeLsum': 0.2713738809702273} bert= {'precision': 0.7676798492670059, 'recall': 0.7191876947879792, 'f1': 0.7423095703125} mover = 0.6047207310084797

sam1120/parking-utcustom-eval

--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 79902058.0 num_examples: 29 download_size: 22213204 dataset_size: 79902058.0 --- # Dataset Card for "parking-utcustom-eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pharaouk/stack-v2-python

--- dataset_info: features: - name: repo_name dtype: string - name: repo_url dtype: string - name: snapshot_id dtype: string - name: revision_id dtype: string - name: directory_id dtype: string - name: branch_name dtype: string - name: visit_date dtype: timestamp[ns] - name: revision_date dtype: timestamp[ns] - name: committer_date dtype: timestamp[ns] - name: github_id dtype: int64 - name: star_events_count dtype: int64 - name: fork_events_count dtype: int64 - name: gha_license_id dtype: string - name: gha_created_at dtype: timestamp[ns] - name: gha_updated_at dtype: timestamp[ns] - name: gha_pushed_at dtype: timestamp[ns] - name: gha_language dtype: string - name: files list: - name: blob_id dtype: string - name: path dtype: string - name: content_id dtype: string - name: language dtype: string - name: length_bytes dtype: int64 - name: detected_licenses sequence: string - name: license_type dtype: string - name: src_encoding dtype: string - name: is_vendor dtype: bool - name: is_generated dtype: bool - name: alphanum_fraction dtype: float32 - name: alpha_fraction dtype: float32 - name: num_lines dtype: int32 - name: avg_line_length dtype: float32 - name: max_line_length dtype: int32 - name: num_files dtype: int64 splits: - name: train num_bytes: 20887324838.790043 num_examples: 8954903 download_size: 15102959847 dataset_size: 20887324838.790043 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/hanako_bluearchive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of hanako/浦和ハナコ/花子 (Blue Archive) This is the dataset of hanako/浦和ハナコ/花子 (Blue Archive), containing 500 images and their tags. The core tags of this character are `long_hair, pink_hair, halo, ahoge, breasts, green_eyes, pink_halo, braid, bow, large_breasts, hair_bow, white_bow, hair_between_eyes, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 1.04 GiB | [Download](https://huggingface.co/datasets/CyberHarem/hanako_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 868.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hanako_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1352 | 1.76 GiB | [Download](https://huggingface.co/datasets/CyberHarem/hanako_bluearchive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/hanako_bluearchive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 24 | ![](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, cleavage, holding_hose, long_sleeves, looking_at_viewer, official_alternate_costume, solo, white_shirt, bikini_under_clothes, blush, smile, collarbone, collared_shirt, wet_shirt, closed_mouth, see-through, water, white_background, simple_background, thighs, pink_bikini, sitting | | 1 | 22 | ![](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, bikini_under_clothes, blue_sky, cleavage, holding_hose, long_sleeves, looking_at_viewer, official_alternate_costume, outdoors, smile, solo, white_shirt, blush, day, water, cloud, pink_bikini, closed_mouth, collarbone, collared_shirt, wet_shirt, see-through, thighs | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bikini_top_only, blush, bottomless, cleavage, collarbone, holding_hose, long_sleeves, looking_at_viewer, navel, official_alternate_costume, open_shirt, smile, solo, stomach, thighs, white_shirt, closed_mouth, collared_shirt, pink_bikini, simple_background, wet, water, white_background, huge_breasts, underboob | | 3 | 36 | ![](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) | short_sleeves, 1girl, solo, blush, smile, white_skirt, looking_at_viewer, pleated_skirt, white_shirt, blue_sailor_collar, single_braid, white_serafuku, pink_bow, closed_mouth, simple_background, white_background, open_mouth | | 4 | 7 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1boy, 1girl, blush, hetero, nipples, solo_focus, sweat, completely_nude, looking_at_viewer, open_mouth, penis, single_braid, vaginal, collarbone, heavy_breathing, huge_breasts, navel, pussy, smile, thighs, motion_lines, side_braid, stomach, bar_censor, blurry_background, cowgirl_position, girl_on_top, happy_sex, heart, indoors, mosaic_censoring, pov_crotch, speech_bubble, spread_legs | | 5 | 5 | ![](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, bare_shoulders, blue_one-piece_swimsuit, blush, competition_school_swimsuit, double-parted_bangs, highleg_swimsuit, looking_at_viewer, side_braid, single_braid, smile, straight_hair, thighs, closed_mouth, from_side, groin, sideboob, solo, black_one-piece_swimsuit, cowboy_shot, standing, arms_behind_back, ass, blue_sky, blurry_background, bright_pupils, chain-link_fence, covered_navel, day, depth_of_field, gradient_hair, outdoors, pool, taut_clothes, wet_swimsuit, white_ribbon | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | holding_hose | long_sleeves | looking_at_viewer | official_alternate_costume | solo | white_shirt | bikini_under_clothes | blush | smile | collarbone | collared_shirt | wet_shirt | closed_mouth | see-through | water | white_background | simple_background | thighs | pink_bikini | sitting | blue_sky | outdoors | day | cloud | bikini_top_only | bottomless | navel | open_shirt | stomach | wet | huge_breasts | underboob | short_sleeves | white_skirt | pleated_skirt | blue_sailor_collar | single_braid | white_serafuku | pink_bow | open_mouth | 1boy | hetero | nipples | solo_focus | sweat | completely_nude | penis | vaginal | heavy_breathing | pussy | motion_lines | side_braid | bar_censor | blurry_background | cowgirl_position | girl_on_top | happy_sex | heart | indoors | mosaic_censoring | pov_crotch | speech_bubble | spread_legs | bare_shoulders | blue_one-piece_swimsuit | competition_school_swimsuit | double-parted_bangs | highleg_swimsuit | straight_hair | from_side | groin | sideboob | black_one-piece_swimsuit | cowboy_shot | standing | arms_behind_back | ass | bright_pupils | chain-link_fence | covered_navel | depth_of_field | gradient_hair | pool | taut_clothes | wet_swimsuit | white_ribbon | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:---------------|:---------------|:--------------------|:-----------------------------|:-------|:--------------|:-----------------------|:--------|:--------|:-------------|:-----------------|:------------|:---------------|:--------------|:--------|:-------------------|:--------------------|:---------|:--------------|:----------|:-----------|:-----------|:------|:--------|:------------------|:-------------|:--------|:-------------|:----------|:------|:---------------|:------------|:----------------|:--------------|:----------------|:---------------------|:---------------|:-----------------|:-----------|:-------------|:-------|:---------|:----------|:-------------|:--------|:------------------|:--------|:----------|:------------------|:--------|:---------------|:-------------|:-------------|:--------------------|:-------------------|:--------------|:------------|:--------|:----------|:-------------------|:-------------|:----------------|:--------------|:-----------------|:--------------------------|:------------------------------|:----------------------|:-------------------|:----------------|:------------|:--------|:-----------|:---------------------------|:--------------|:-----------|:-------------------|:------|:----------------|:-------------------|:----------------|:-----------------|:----------------|:-------|:---------------|:---------------|:---------------| | 0 | 24 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 22 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | X | | X | X | X | X | | X | | X | X | X | X | X | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 36 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 7 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | | X | | | | | X | X | X | | | | | | | | X | | | | | | | | | X | | X | | X | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | | X | | X | | | X | X | | | | X | | | | | X | | | X | X | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

AlekseyKorshuk/dalio-book-handwritten-io-sorted

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string splits: - name: train num_bytes: 674482.0 num_examples: 442 - name: validation num_bytes: 519665 num_examples: 315 - name: test num_bytes: 14786 num_examples: 10 download_size: 0 dataset_size: 1208933.0 --- # Dataset Card for "dalio-book-handwritten-io-sorted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mainlp/inconsistencies_companies

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

Mr-User/many

--- license: apache-2.0 ---

echarlaix/vqa

--- license: apache-2.0 ---

satanicsmores/IMAGE2IMAGE-SatTANIC-SMores

--- license: mit language: - en - gl - is tags: - art - code - not-for-all-audiences pretty_name: pretty_name size_categories: - n<1K ---

amansahanigermany/cartoonizer-dataset

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 6497455.0 num_examples: 10 download_size: 6500440 dataset_size: 6497455.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ttxy/kaggle

--- license: apache-2.0 --- kaggle datasets

Megnis/ml_talents_hr-llamma2-style

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 7708951 num_examples: 656 download_size: 2288316 dataset_size: 7708951 configs: - config_name: default data_files: - split: train path: data/train-* ---

NeuraXenetica/managpt-4080-nlp-prompts-and-generated-texts

--- license: cc-by-4.0 task_categories: - text-generation language: - en pretty_name: 'ManaGPT: 4,080 NLP prompts and generated texts' size_categories: - 1K<n<10K --- This dataset includes 4,080 texts that were generated by the [**ManaGPT-1020**](https://huggingface.co/NeuraXenetica/ManaGPT-1020) large language model, in response to particular input sequences. ManaGPT-1020 is a free, open-source model available for download and use via Hugging Face’s “transformers” Python package. The model is a 1.5-billion-parameter LLM that’s capable of generating text in order to complete a sentence whose first words have been provided via a user-supplied input sequence. The model represents an elaboration of GPT-2 that has been fine-tuned (using Python and TensorFlow) on a specialized English-language corpus of over 509,000 words from the domain of organizational futures studies. In particular, the model has been trained to generate analysis, predictions, and recommendations regarding the emerging role of advanced AI, social robotics, ubiquitous computing, virtual reality, neurocybernetic augmentation, and other “posthumanizing” technologies in organizational life. In generating the texts, 102 different prompts were used, each of which was employed to generate 20 responses. The 102 input sequences were created by concatenating 12 different "subjects" with 17 different "modal variants," in every possible combination. The subjects included 6 grammatically singular subjects: - "The workplace of tomorrow" - "Technological posthumanization" - "The organizational use of AI" - "A robotic boss" - "An artificially intelligent coworker" - "Business culture within Society 5.0" Also included were 6 grammatically plural subjects: - "Social robots" - "Hybrid human-robotic organizations" - "Artificially intelligent businesses" - "The posthumanized workplaces of the future" - "Cybernetically augmented workers" - "Organizations in Society 5.0" For the 6 grammatically singular subjects, the 17 modal variants included one "blank" variant (an empty string) and 16 phrases that lend the input sequence diverse forms of "modal shading," by indicating varying degrees of certainty, probability, predictability, logical necessity, or moral obligation or approbation. These modal variants were: - "" - " is" - " is not" - " will" - " will be" - " may" - " might never" - " is likely to" - " is unlikely to" - " should" - " can" - " cannot" - " can never" - " must" - " must not" - " is like" - " will be like" The variants used with grammatically plural subjects were identical, apart from the fact that the word “is” was changed to “are,” wherever it appeared. In a small number of cases (only occurring when the empty string "" was used as part of the input sequence), the model failed to generate any output beyond the input sequence itself.

kz919/open-orca-flan-50k-synthetic-reward-e5-mistral-7b-instruct-v7

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: task dtype: string - name: ignos-Mistral-T5-7B-v1 dtype: string - name: cognAI-lil-c3po dtype: string - name: viethq188-Rabbit-7B-DPO-Chat dtype: string - name: cookinai-DonutLM-v1 dtype: string - name: v1olet-v1olet-merged-dpo-7B dtype: string - name: normalized_rewards sequence: float64 - name: router_label dtype: int64 splits: - name: train num_bytes: 16166616 num_examples: 7271 download_size: 7453727 dataset_size: 16166616 configs: - config_name: default data_files: - split: train path: data/train-* ---

cakiki/stack-smol-xxl-embeddings

--- dataset_info: features: - name: token_ids sequence: int64 - name: lri_160 sequence: float64 splits: - name: train num_bytes: 231978165104 num_examples: 11658586 download_size: 34909750705 dataset_size: 231978165104 --- # Dataset Card for "stack-smol-xxl-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-staging-eval-project-emotion-2d469b4f-13675887

--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: [] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

RustamovPY/test_voices

--- dataset_info: features: - name: file_name dtype: string - name: voice dtype: string - name: text dtype: string - name: speaker dtype: string splits: - name: train num_bytes: 385 num_examples: 3 download_size: 2746 dataset_size: 385 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test_voices" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/dioscuri_pollux_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Dioscuri Pollux (Fate/Grand Order) This is the dataset of Dioscuri Pollux (Fate/Grand Order), containing 131 images and their tags. The core tags of this character are `blonde_hair, bangs, breasts, medium_hair, blue_eyes, small_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 131 | 152.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 131 | 98.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 311 | 202.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 131 | 136.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 311 | 266.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_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/dioscuri_pollux_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 | 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, armlet, bare_shoulders, diadem, looking_at_viewer, metal_collar, pauldrons, solo, white_robe, halterneck, thighs, black_shirt, bracer, sword, closed_mouth, faulds, simple_background | | 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, armlet, bare_shoulders, blush, bracer, covered_navel, diadem, halterneck, looking_at_viewer, medium_breasts, simple_background, solo, thighs, white_background, metal_collar, purple_eyes, smile, closed_mouth, white_robe, faulds | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, armlet, black_shirt, diadem, looking_at_viewer, metal_collar, short_hair, twins, white_robe, 1boy, bare_shoulders, brother_and_sister, simple_background, white_background, pauldrons, halterneck, smile | | 3 | 17 | ![](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) | 1boy, 1girl, blush, hetero, nipples, thighs, diadem, large_breasts, open_mouth, collarbone, penis, armlet, bar_censor, pussy, vaginal, bare_shoulders, girl_on_top, nude, purple_eyes, sex_from_behind, smile, speech_bubble, straddling | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armlet | bare_shoulders | diadem | looking_at_viewer | metal_collar | pauldrons | solo | white_robe | halterneck | thighs | black_shirt | bracer | sword | closed_mouth | faulds | simple_background | blush | covered_navel | medium_breasts | white_background | purple_eyes | smile | short_hair | twins | 1boy | brother_and_sister | hetero | nipples | large_breasts | open_mouth | collarbone | penis | bar_censor | pussy | vaginal | girl_on_top | nude | sex_from_behind | speech_bubble | straddling | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-----------------|:---------|:--------------------|:---------------|:------------|:-------|:-------------|:-------------|:---------|:--------------|:---------|:--------|:---------------|:---------|:--------------------|:--------|:----------------|:-----------------|:-------------------|:--------------|:--------|:-------------|:--------|:-------|:---------------------|:---------|:----------|:----------------|:-------------|:-------------|:--------|:-------------|:--------|:----------|:--------------|:-------|:------------------|:----------------|:-------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | | X | X | | X | | | | | X | | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | 3 | 17 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | | | | | | X | | | | | | | X | | | | X | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

HydraLM/GPTeacher-General-Instruct_list_dict

--- dataset_info: features: - name: conversations list: - name: input dtype: string - name: instruction dtype: string - name: response dtype: string - name: conversation_id dtype: int64 splits: - name: train num_bytes: 56447404 num_examples: 89259 download_size: 0 dataset_size: 56447404 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPTeacher-General-Instruct_list_dict" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

presencesw/cot-collection_v2

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: rationale dtype: string - name: task dtype: string - name: type dtype: string splits: - name: train num_bytes: 1427400995.4030628 num_examples: 1271010 download_size: 546101259 dataset_size: 1427400995.4030628 configs: - config_name: default data_files: - split: train path: data/train-* ---

AntoineBlanot/mnli-3way

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label_name dtype: string splits: - name: train num_bytes: 75405059 num_examples: 392702 - name: test num_bytes: 1853683 num_examples: 9815 download_size: 51216284 dataset_size: 77258742 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "mnli-3way" This dataset is the [multi_nli](https://huggingface.co/datasets/multi_nli) dataset where the labels are: `entailment`, `contradiction` and `neutral`. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jlbaker361/tex_inv_hot_ip_light

--- dataset_info: features: - name: label dtype: string - name: tex_inv_hot_ip_prompt_similarity dtype: float32 - name: tex_inv_hot_ip_identity_consistency dtype: float32 - name: tex_inv_hot_ip_negative_prompt_similarity dtype: float32 - name: tex_inv_hot_ip_target_prompt_similarity dtype: float32 - name: tex_inv_hot_ip_aesthetic_score dtype: float32 splits: - name: train num_bytes: 308 num_examples: 11 download_size: 4221 dataset_size: 308 configs: - config_name: default data_files: - split: train path: data/train-* ---

amttl

--- annotations_creators: - crowdsourced language_creators: - found language: - zh license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - parsing pretty_name: AMTTL dataset_info: config_name: amttl features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: class_label: names: '0': B '1': I '2': E '3': S splits: - name: train num_bytes: 1132196 num_examples: 3063 - name: validation num_bytes: 324358 num_examples: 822 - name: test num_bytes: 328509 num_examples: 908 download_size: 274351 dataset_size: 1785063 configs: - config_name: amttl data_files: - split: train path: amttl/train-* - split: validation path: amttl/validation-* - split: test path: amttl/test-* default: true --- # Dataset Card for AMTTL ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - **Repository:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data) - **Paper:** [Aclweb](http://aclweb.org/anthology/C18-1307) - **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 ```bibtex @inproceedings{xing2018adaptive, title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text}, author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian}, booktitle={Proceedings of the 27th International Conference on Computational Linguistics}, pages={3619--3630}, year={2018} } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.

leoozyhg/12312123

--- license: openrail ---

GAIR/preference-dissection

--- dataset_info: features: - name: query dtype: string - name: scenario_auto-j dtype: string - name: scenario_group dtype: string - name: response_1 struct: - name: content dtype: string - name: model dtype: string - name: num_words dtype: int64 - name: response_2 struct: - name: content dtype: string - name: model dtype: string - name: num_words dtype: int64 - name: gpt-4-turbo_reference dtype: string - name: clear intent dtype: string - name: explicitly express feelings dtype: string - name: explicit constraints sequence: string - name: explicit subjective stances sequence: string - name: explicit mistakes or biases sequence: string - name: preference_labels struct: - name: gpt-3.5-turbo-1106 dtype: string - name: gpt-4-1106-preview dtype: string - name: human dtype: string - name: llama-2-13b dtype: string - name: llama-2-13b-chat dtype: string - name: llama-2-70b dtype: string - name: llama-2-70b-chat dtype: string - name: llama-2-7b dtype: string - name: llama-2-7b-chat dtype: string - name: mistral-7b dtype: string - name: mistral-7b-instruct-v0.1 dtype: string - name: mistral-7b-instruct-v0.2 dtype: string - name: mistral-8x7b dtype: string - name: mistral-8x7b-instruct-v0.1 dtype: string - name: qwen-14b dtype: string - name: qwen-14b-chat dtype: string - name: qwen-72b dtype: string - name: qwen-72b-chat dtype: string - name: qwen-7b dtype: string - name: qwen-7b-chat dtype: string - name: tulu-2-dpo-13b dtype: string - name: tulu-2-dpo-70b dtype: string - name: tulu-2-dpo-7b dtype: string - name: vicuna-13b-v1.5 dtype: string - name: vicuna-7b-v1.5 dtype: string - name: wizardLM-13b-v1.2 dtype: string - name: wizardLM-70b-v1.0 dtype: string - name: yi-34b dtype: string - name: yi-34b-chat dtype: string - name: yi-6b dtype: string - name: yi-6b-chat dtype: string - name: zephyr-7b-alpha dtype: string - name: zephyr-7b-beta dtype: string - name: basic_response_1 struct: - name: admit limitations or mistakes dtype: int64 - name: authoritative tone dtype: int64 - name: clear and understandable dtype: int64 - name: complex word usage and sentence structure dtype: int64 - name: friendly dtype: int64 - name: funny and humorous dtype: int64 - name: grammar, spelling, punctuation, and code-switching dtype: int64 - name: harmlessness dtype: int64 - name: information richness without considering inaccuracy dtype: int64 - name: innovative and novel dtype: int64 - name: interactive dtype: int64 - name: metaphors, personification, similes, hyperboles, irony, parallelism dtype: int64 - name: persuade user dtype: int64 - name: polite dtype: int64 - name: relevance without considering inaccuracy dtype: int64 - name: repetitive dtype: int64 - name: step by step solution dtype: int64 - name: use of direct and explicit supporting materials dtype: int64 - name: use of informal expressions dtype: int64 - name: well formatted dtype: int64 - name: basic_response_2 struct: - name: admit limitations or mistakes dtype: int64 - name: authoritative tone dtype: int64 - name: clear and understandable dtype: int64 - name: complex word usage and sentence structure dtype: int64 - name: friendly dtype: int64 - name: funny and humorous dtype: int64 - name: grammar, spelling, punctuation, and code-switching dtype: int64 - name: harmlessness dtype: int64 - name: information richness without considering inaccuracy dtype: int64 - name: innovative and novel dtype: int64 - name: interactive dtype: int64 - name: metaphors, personification, similes, hyperboles, irony, parallelism dtype: int64 - name: persuade user dtype: int64 - name: polite dtype: int64 - name: relevance without considering inaccuracy dtype: int64 - name: repetitive dtype: int64 - name: step by step solution dtype: int64 - name: use of direct and explicit supporting materials dtype: int64 - name: use of informal expressions dtype: int64 - name: well formatted dtype: int64 - name: errors_response_1 struct: - name: applicable or not dtype: string - name: errors list: - name: brief description dtype: string - name: severity dtype: string - name: type dtype: string - name: errors_response_2 struct: - name: applicable or not dtype: string - name: errors list: - name: brief description dtype: string - name: severity dtype: string - name: type dtype: string - name: query-specific_response_1 struct: - name: clarify user intent dtype: int64 - name: correcting explicit mistakes or biases sequence: string - name: satisfying explicit constraints sequence: string - name: showing empathetic dtype: int64 - name: supporting explicit subjective stances sequence: string - name: query-specific_response_2 struct: - name: clarify user intent dtype: int64 - name: correcting explicit mistakes or biases sequence: string - name: satisfying explicit constraints sequence: string - name: showing empathetic dtype: int64 - name: supporting explicit subjective stances sequence: string splits: - name: train num_bytes: 27617371 num_examples: 5240 download_size: 13124269 dataset_size: 27617371 configs: - config_name: default data_files: - split: train path: data/train-* language: - en pretty_name: Preference Dissection license: cc-by-nc-4.0 --- ## Introduction We release the annotated data used in [Dissecting Human and LLM Preferences](https://arxiv.org/abs/2402.11296). *Original Dataset* - The dataset is based on [lmsys/chatbot_arena_conversations](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations), which contains 33K cleaned conversations with pairwise human preferences collected from 13K unique IP addresses on the [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) from April to June 2023. *Filtering and Scenario-wise Sampling* - We filter out the conversations that are not in English, with "Tie" or "Both Bad" labels, and the multi-turn conversations. We first sample 400 samples with unsafe queries according to the OpenAI moderation API tags and the additional toxic tags in the original dataset, then we apply [Auto-J's scenario classifier](https://huggingface.co/GAIR/autoj-scenario-classifier) to determine the scenario of each sample (we merge the Auto-J's scenarios into 10 new ones). For the *Knowledge-aware* and *Others* scenarios, we pick 820 samples, and for the other scenarios, we pick 400 samples. The total number is 5,240. *Collecting Preferences* - Besides the human preference labels in this original dataset, we also collect the binary preference labels from 32 LLMs, including 2 proprietary LLMs and 30 open-source ones. *Annotation on Defined Properties* - We define a set of 29 properties, we annotate how each property is satisfied (in Likert scale rating or property-specific annotation) in all responses ($5,240\times 2=10,480$). See our paper for more details of the defined properties. ## Dataset Overview An example of the json format is as follows: ```json { "query": "...", "scenario_auto-j": "...", "scenario_group": "...", "response_1": { "content": "...", "model": "...", "num_words": "..." }, "response_2": {...}, "gpt-4-turbo_reference": "...", "clear intent": "Yes/No", "explicitly express feelings": "Yes/No", "explicit constraints": [ ... ], "explicit subjective stances": [ ... ], "explicit mistakes or biases": [ ... ], "preference_labels": { "human": "response_1/response_2", "gpt-4-turbo": "response_1/response_2", ... }, "basic_response_1": { "admit limitations or mistakes": 0/1/2/3, "authoritative tone": 0/1/2/3, ... }, "basic_response_2": {...}, "errors_response_1": { "applicable or not": "applicable/not applicable", "errors":[ { "brief description": "...", "severity": "severe/moderate/minor", "type": "...", }, ... ] }, "errors_response_2": {...}, "query-specific_response_1": { "clarify user intent": ..., "correcting explicit mistakes or biases": None, "satisfying explicit constraints": [ ... ], "showing empathetic": [ ... ], "supporting explicit subjective stances": [ ... ] }, "query-specific_response_2": {...} } ``` The following fields are basic information: - **query**: The user query. - **scenario_auto-j**: The scenario classified by Auto-J's classifier. - **scenario_group**: One of the 10 new scenarios we merged from the Auto-J's scenario, including an *Unsafe Query* scenario. - **response_1/response_2**: The content of a response: - **content**: The text content. - **model**: The model that generate this response. - **num_words**: The number of words of this response, determined by NLTK. - **gpt-4-turbo_reference**: An reference response generated by GPT-4-Turbo. The following fields are Query-Specific prerequisites. For the last three, there may be an empty list if there is no constraints/stances/mistakes. - **clear intent**: Whether the intent of the user is clearly expressed in the query, "Yes" or "No". - **explicitly express feelings**: Whether the user clearly express his/her feelings or emotions in the query, "Yes" or "No". - **explicit constraints**": A list containing all the explicit constraints in the query. - **explicit subjective stances**: A list containing all the subjective stances in the query. - **explicit mistakes or biases**: A list containing all the mistakes or biases in the query. The following fields are the main body of the annotation. - **preference_labels**: The preference label for each judge (human or an LLM) indicating which response is preferred in a pair, "response_1/response_2". - **basic_response_1/basic_response_2**: The annotated ratings of the 20 basic properties (except *lengthy*) for the response. - **property_name**: 0/1/2/3 - ... - **errors_response_1/errors_response_2**: The detected errors of the response. - **applicable or not**: If GPT-4-Turbo find itself can reliably detect the errors in the response. - **errors**: A list containing the detected errors in the response. - **brief description**: A brief description of the error. - **severity**: How much the error affect the overall correctness of the response, "severe/moderate/minor". - **type**: The type of the error, "factual error/information contradiction to the query/math operation error/code generation error" - **query-specific_response_1/query-specific_response_2**: The annotation results of the Query-Specific properties. - **clarify user intent**: If the user intent is not clear, rate how much the response help clarify the intent, 0/1/2/3. - **showing empathetic**: If the user expresses feelings or emotions, rate how much the response show empathetic, 0/1/2/3. - **satisfying explicit constraints**: If there are explicit constraints in the query, rate how much the response satisfy each of them. - A list of "{description of constraint} | 0/1/2/3" - **correcting explicit mistakes or biases**: If there are mistakes of biases in the query, classify how the response correct each of them - A list of "{description of mistake} | Pointed out and corrected/Pointed out but not corrected/Corrected without being pointed out/Neither pointed out nor corrected" - **supporting explicit subjective stances**: If there are subject stances in the query, classify how the response support each of them - A list of "{description of stance} | Strongly supported/Weakly supported/Neutral/Weakly opposed/Strongly opposed" ## Statistics 👇 Number of samples meeting 5 Query-specific prerequisites. | Prerequisite | # | Prerequisite | # | | ------------------------- | ----- | ---------------- | ---- | | with explicit constraints | 1,418 | unclear intent | 459 | | show subjective stances | 388 | express feelings | 121 | | contain mistakes or bias | 401 | | | 👇 Mean Score/Count for each property in collected data. *The average scores of 5 query-specific properties are calculated only on samples where the queries met specific prerequisites. | Property | Mean Score/Count | Property | Mean Score/Count | | ---------------------------- | ---------------- | ---------------------------- | ---------------- | | **Mean Score** | | | harmless | 2.90 | persuasive | 0.27 | | grammarly correct | 2.70 | step-by-step | 0.37 | | friendly | 1.79 | use informal expressions | 0.04 | | polite | 2.78 | clear | 2.54 | | interactive | 0.22 | contain rich information | 1.74 | | authoritative | 1.67 | novel | 0.47 | | funny | 0.08 | relevant | 2.45 | | use rhetorical devices | 0.16 | clarify intent* | 1.33 | | complex word & sentence | 0.89 | show empathetic* | 1.48 | | use supporting materials | 0.13 | satisfy constraints* | 2.01 | | well formatted | 1.26 | support stances* | 2.28 | | admit limits | 0.17 | correct mistakes* | 1.08 | | **Mean Count** | | | severe errors | 0.59 | minor errors | 0.23 | | moderate errors | 0.61 | length | 164.52 | 👇 Property correlation in the annotated data. <img src="./property_corr.PNG" alt="image-20240213145030747" style="zoom: 50%;" /> ## Disclaimers and Terms **This part is copied from the original dataset* - **This dataset contains conversations that may be considered unsafe, offensive, or upsetting.** It is not intended for training dialogue agents without applying appropriate filtering measures. We are not responsible for any outputs of the models trained on this dataset. - Statements or opinions made in this dataset do not reflect the views of researchers or institutions involved in the data collection effort. - Users of this data are responsible for ensuring its appropriate use, which includes abiding by any applicable laws and regulations. - Users of this data should adhere to the terms of use for a specific model when using its direct outputs. - Users of this data agree to not attempt to determine the identity of individuals in this dataset. ## License Following the original dataset, this dataset is licensed under CC-BY-NC-4.0. ## Citation ``` @article{li2024dissecting, title={Dissecting Human and LLM Preferences}, author={Li, Junlong and Zhou, Fan and Sun, Shichao and Zhang, Yikai and Zhao, Hai and Liu, Pengfei}, journal={arXiv preprint arXiv:2402.11296}, year={2024} } ```

the-french-artist/wikipedia_20220301.simple_sentence_embeddings

--- license: apache-2.0 dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: sentence_index dtype: int64 - name: line_index dtype: int64 - name: embedding sequence: float32 splits: - name: train num_bytes: 1061056337 num_examples: 585455 download_size: 1331697726 dataset_size: 1061056337 configs: - config_name: default data_files: - split: train path: data/train-* ---

YUiCHl/scale0

--- dataset_info: features: - name: image dtype: image - name: conditioning dtype: image - name: caption dtype: string splits: - name: train num_bytes: 474801995.0 num_examples: 1588 download_size: 472199271 dataset_size: 474801995.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

hopee4/cariuchas

--- license: openrail ---

gagan3012/arabictext

--- dataset_info: features: - name: text dtype: string - name: check_char_repetition_criteria dtype: float64 - name: check_flagged_words_criteria dtype: float64 - name: meta_data dtype: string - name: __id__ dtype: int64 - name: duplicate dtype: bool - name: char dtype: int64 - name: Arabic_char dtype: int64 - name: latin_char dtype: int64 - name: numbers_char dtype: int64 - name: puc_char dtype: int64 splits: - name: train num_bytes: 471530 num_examples: 1000 download_size: 231211 dataset_size: 471530 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "arabictext" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

davanstrien/autotrain-data-ner-test

Invalid username or password.

cyzgab/singlish-to-english-synthetic

--- license: cc-by-nc-sa-4.0 task_categories: - translation language: - en pretty_name: Singlish to English 🇸🇬 size_categories: - n<1K --- # Singlish to English 🇸🇬 > Singapore is known for its efficiency and Singlish is no different - it's colourful and snappy. - [Tessa Wong, BBC News, 2015](https://www.bbc.com/news/magazine-33809914) This is a synthetic dataset generated by GPT-4. Each json pair contains one Singlish sentence about an everyday activity (e.g. cooking) and its English translation. # Sample entry ```json singlish: "Eh, chop the garlic - you can a not?", english: Hey, do you know how to chop the garlic?" ``` # Data Generation Code ```python import json import pandas as pd from openai import OpenAI client = OpenAI() NUM_SAMPLE = 10 ACTIVITIES = ['cooking', 'studying', 'sleeping', 'eating', 'working', 'exercising', 'reading', 'cleaning', 'shopping', 'driving', 'walking', 'bathing', 'going to work', 'listening to music', 'watching TV', 'playing video games', 'using a computer', 'texting', 'socializing', 'meditating', 'commuting', 'doing laundry', 'ironing clothes', 'dusting', 'vacuuming', 'painting', 'drawing', 'grocery shopping', 'sewing', 'taking a nap', 'jogging', 'biking', 'swimming', 'playing sports', 'checking emails', 'playing with children', 'watching movies', 'playing board games', 'attending school or classes', 'going to the gym', 'playing a musical instrument', 'singing', 'dancing', 'writing', 'photography', 'traveling', 'visiting friends', 'attending events', 'volunteering', 'attending meetings'] dataset = {} for index, activity in enumerate(ACTIVITIES): print(index, activity) response = client.chat.completions.create( model="gpt-4-1106-preview", messages=[{"role": "system", "content": "You are an expert in translating Singlish to English"}, {"role": "user", "content": f"Create {NUM_SAMPLE} random Singlish (s) to English (e) translation pairs in json. Write full sentences about {activity}."\ f"Don't exaggerate the use of Singlish, and be natural, as how a real Singaporean would speak."\ f"Start the keys from {(index*NUM_SAMPLE)+1}. For example,"\ "{'X':{'s': 'aiyo, why like that', 'e': 'oh my, how did this happen'}"\ "..., 'X+5': {'s': 'don't play play', 'e': 'don't fool around'} }"}], temperature=0.01, response_format={"type":"json_object"} ) output = response.choices[0].message.content output_json = json.loads(output) dataset.update(output_json) # Save the current state of the combined dictionary with open('singlish_to_english_v0.1.json', 'w') as f: json.dump(dataset, f, indent=None) # Convert to tabular csv df = pd.read_json("singlish_to_english_v0.1.json") df = df.T df = df.reset_index() df.columns = ["index", "singlish", "english"] df.to_csv("singlish_to_english_v0.1.csv", index=False) ```

open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1

--- pretty_name: Evaluation run of Replete-AI/Mistral-Evolved-11b-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Replete-AI/Mistral-Evolved-11b-v0.1](https://huggingface.co/Replete-AI/Mistral-Evolved-11b-v0.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T15:15:50.568647](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1/blob/main/results_2024-03-21T15-15-50.568647.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\": 0.6309829529704917,\n\ \ \"acc_stderr\": 0.03249502226073932,\n \"acc_norm\": 0.6345615860197364,\n\ \ \"acc_norm_stderr\": 0.033137530512338635,\n \"mc1\": 0.4320685434516524,\n\ \ \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5923114451952954,\n\ \ \"mc2_stderr\": 0.016045963776594944\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5989761092150171,\n \"acc_stderr\": 0.014322255790719867,\n\ \ \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.014169664520303101\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6646086436964748,\n\ \ \"acc_stderr\": 0.004711622011148463,\n \"acc_norm\": 0.8465445130452102,\n\ \ \"acc_norm_stderr\": 0.0035968938961909113\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.042446332383532265\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\ \ \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6415094339622641,\n \"acc_stderr\": 0.02951470358398176,\n\ \ \"acc_norm\": 0.6415094339622641,\n \"acc_norm_stderr\": 0.02951470358398176\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.024251071262208837,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.024251071262208837\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\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\": 0.8,\n \"acc_stderr\": 0.031234752377721164,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721164\n \ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.02805779167298901,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.02805779167298901\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131154,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131154\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163224,\n \"\ acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163224\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"\ acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437395,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437395\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159274,\n \ \ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159274\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n\ \ \"acc_stderr\": 0.03050028317654585,\n \"acc_norm\": 0.7085201793721974,\n\ \ \"acc_norm_stderr\": 0.03050028317654585\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\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\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077816,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077816\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8007662835249042,\n\ \ \"acc_stderr\": 0.014283378044296417,\n \"acc_norm\": 0.8007662835249042,\n\ \ \"acc_norm_stderr\": 0.014283378044296417\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.20446927374301677,\n\ \ \"acc_stderr\": 0.01348881340471193,\n \"acc_norm\": 0.20446927374301677,\n\ \ \"acc_norm_stderr\": 0.01348881340471193\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n\ \ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.025494259350694905,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.025494259350694905\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.02532988817190092,\n\ \ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.02532988817190092\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\": 0.4680573663624511,\n\ \ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\ \ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6985294117647058,\n \"acc_stderr\": 0.027875982114273168,\n\ \ \"acc_norm\": 0.6985294117647058,\n \"acc_norm_stderr\": 0.027875982114273168\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6372549019607843,\n \"acc_stderr\": 0.01945076843250552,\n \ \ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.01945076843250552\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.02950489645459596,\n\ \ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.02950489645459596\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.02740385941078684,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.02740385941078684\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338734,\n\ \ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338734\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4320685434516524,\n\ \ \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5923114451952954,\n\ \ \"mc2_stderr\": 0.016045963776594944\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174789\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4981046247156937,\n \ \ \"acc_stderr\": 0.013772385765569753\n }\n}\n```" repo_url: https://huggingface.co/Replete-AI/Mistral-Evolved-11b-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|arc:challenge|25_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T15-15-50.568647.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|gsm8k|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hellaswag|10_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-15-50.568647.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-15-50.568647.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T15-15-50.568647.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T15_15_50.568647 path: - '**/details_harness|winogrande|5_2024-03-21T15-15-50.568647.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T15-15-50.568647.parquet' - config_name: results data_files: - split: 2024_03_21T15_15_50.568647 path: - results_2024-03-21T15-15-50.568647.parquet - split: latest path: - results_2024-03-21T15-15-50.568647.parquet --- # Dataset Card for Evaluation run of Replete-AI/Mistral-Evolved-11b-v0.1  Dataset automatically created during the evaluation run of model [Replete-AI/Mistral-Evolved-11b-v0.1](https://huggingface.co/Replete-AI/Mistral-Evolved-11b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T15:15:50.568647](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Mistral-Evolved-11b-v0.1/blob/main/results_2024-03-21T15-15-50.568647.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": 0.6309829529704917, "acc_stderr": 0.03249502226073932, "acc_norm": 0.6345615860197364, "acc_norm_stderr": 0.033137530512338635, "mc1": 0.4320685434516524, "mc1_stderr": 0.017341202394988257, "mc2": 0.5923114451952954, "mc2_stderr": 0.016045963776594944 }, "harness|arc:challenge|25": { "acc": 0.5989761092150171, "acc_stderr": 0.014322255790719867, "acc_norm": 0.6220136518771331, "acc_norm_stderr": 0.014169664520303101 }, "harness|hellaswag|10": { "acc": 0.6646086436964748, "acc_stderr": 0.004711622011148463, "acc_norm": 0.8465445130452102, "acc_norm_stderr": 0.0035968938961909113 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.042446332383532265, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.042446332383532265 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395268, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395268 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.04999999999999999, "acc_norm": 0.55, "acc_norm_stderr": 0.04999999999999999 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6415094339622641, "acc_stderr": 0.02951470358398176, "acc_norm": 0.6415094339622641, "acc_norm_stderr": 0.02951470358398176 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958548, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958548 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594963, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594963 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4444444444444444, "acc_stderr": 0.025591857761382182, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.025591857761382182 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7612903225806451, "acc_stderr": 0.024251071262208837, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.024251071262208837 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "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": 0.8, "acc_stderr": 0.031234752377721164, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721164 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8080808080808081, "acc_stderr": 0.02805779167298901, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.02805779167298901 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8652849740932642, "acc_stderr": 0.02463978909770944, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.02463978909770944 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131154, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131154 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977934, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977934 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8385321100917431, "acc_stderr": 0.015776239256163224, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.015776239256163224 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5509259259259259, "acc_stderr": 0.03392238405321617, "acc_norm": 0.5509259259259259, "acc_norm_stderr": 0.03392238405321617 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437395, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437395 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7637130801687764, "acc_stderr": 0.027652153144159274, "acc_norm": 0.7637130801687764, "acc_norm_stderr": 0.027652153144159274 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7085201793721974, "acc_stderr": 0.03050028317654585, "acc_norm": 0.7085201793721974, "acc_norm_stderr": 0.03050028317654585 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "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": 0.7484662576687117, "acc_stderr": 0.034089978868575295, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.034089978868575295 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077816, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077816 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8007662835249042, "acc_stderr": 0.014283378044296417, "acc_norm": 0.8007662835249042, "acc_norm_stderr": 0.014283378044296417 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.20446927374301677, "acc_stderr": 0.01348881340471193, "acc_norm": 0.20446927374301677, "acc_norm_stderr": 0.01348881340471193 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694905, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694905 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7067901234567902, "acc_stderr": 0.02532988817190092, "acc_norm": 0.7067901234567902, "acc_norm_stderr": 0.02532988817190092 }, "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": 0.4680573663624511, "acc_stderr": 0.012744149704869649, "acc_norm": 0.4680573663624511, "acc_norm_stderr": 0.012744149704869649 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6985294117647058, "acc_stderr": 0.027875982114273168, "acc_norm": 0.6985294117647058, "acc_norm_stderr": 0.027875982114273168 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6372549019607843, "acc_stderr": 0.01945076843250552, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.01945076843250552 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425465, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425465 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6938775510204082, "acc_stderr": 0.02950489645459596, "acc_norm": 0.6938775510204082, "acc_norm_stderr": 0.02950489645459596 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.02740385941078684, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.02740385941078684 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.783625730994152, "acc_stderr": 0.03158149539338734, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.03158149539338734 }, "harness|truthfulqa:mc|0": { "mc1": 0.4320685434516524, "mc1_stderr": 0.017341202394988257, "mc2": 0.5923114451952954, "mc2_stderr": 0.016045963776594944 }, "harness|winogrande|5": { "acc": 0.7576953433307024, "acc_stderr": 0.012042352526174789 }, "harness|gsm8k|5": { "acc": 0.4981046247156937, "acc_stderr": 0.013772385765569753 } } ``` ## 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]

kavyamanohar/Malayalam-word-freq

--- license: cc-by-4.0 --- ## Word Frequency Profile of Malayalam The repo contains Malayalam words and their frequencies as obtained from AI4Bharat [Indic NLP corpus](https://github.com/AI4Bharat/indicnlp_corpus). There is an associated python script to plot the word frequnecy profile.

hemantk089/llama2_7b_fine_tuning_complete_dataset_v7

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 301722 num_examples: 813 - name: test num_bytes: 72617 num_examples: 204 download_size: 107905 dataset_size: 374339 --- # Dataset Card for "llama2_7b_fine_tuning_complete_dataset_v7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/koshigaya_natsumi_nonnonbiyori

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Koshigaya Natsumi This is the dataset of Koshigaya Natsumi, containing 300 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 | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 737 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 824 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 737 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 737 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 604 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 824 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 824 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

Code-Refinement/utf_20_refs

--- dataset_info: features: - name: problem_id dtype: string - name: attempt_id dtype: int64 - name: generated_solution dtype: string - name: original_reward dtype: float64 - name: chosen_ref_id dtype: int64 - name: chosen_refinement dtype: string - name: chosen_reward dtype: float64 - name: rejected_ref_id dtype: int64 - name: rejected_refinement dtype: string - name: rejected_reward dtype: float64 splits: - name: train num_bytes: 439039919 num_examples: 303459 - name: test num_bytes: 116366494 num_examples: 68155 download_size: 17812330 dataset_size: 555406413 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---