davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus	--- pretty_name: Evaluation run of lgaalves/llama-2-13b-chat-platypus dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lgaalves/llama-2-13b-chat-platypus](https://huggingface.co/lgaalves/llama-2-13b-chat-platypus)\ \ 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_lgaalves__llama-2-13b-chat-platypus\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T20:27:56.260953](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus/blob/main/results_2023-10-27T20-27-56.260953.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\": 0.0035654362416107383,\n\ \ \"em_stderr\": 0.0006104082299890483,\n \"f1\": 0.06259542785234914,\n\ \ \"f1_stderr\": 0.001452272347431231,\n \"acc\": 0.44182080490769055,\n\ \ \"acc_stderr\": 0.010533564468131328\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890483,\n\ \ \"f1\": 0.06259542785234914,\n \"f1_stderr\": 0.001452272347431231\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.12357846853677028,\n \ \ \"acc_stderr\": 0.009065050306776914\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7600631412786109,\n \"acc_stderr\": 0.01200207862948574\n\ \ }\n}\n```" repo_url: https://huggingface.co/lgaalves/llama-2-13b-chat-platypus 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_12T04_54_55.763898 path: - '/details_harness\|arc:challenge\|25_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-12T04-54-55.763898.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T20_27_56.260953 path: - '/details_harness\|drop\|3_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-27T20-27-56.260953.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T20_27_56.260953 path: - '/details_harness\|gsm8k\|5_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-27T20-27-56.260953.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hellaswag\|10_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T04-54-55.763898.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T04_54_55.763898 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-12T04-54-55.763898.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T20_27_56.260953 path: - '/details_harness\|winogrande\|5_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-27T20-27-56.260953.parquet' - config_name: results data_files: - split: 2023_09_12T04_54_55.763898 path: - results_2023-09-12T04-54-55.763898.parquet - split: 2023_10_27T20_27_56.260953 path: - results_2023-10-27T20-27-56.260953.parquet - split: latest path: - results_2023-10-27T20-27-56.260953.parquet --- # Dataset Card for Evaluation run of lgaalves/llama-2-13b-chat-platypus ## Dataset Description - Homepage: - Repository: https://huggingface.co/lgaalves/llama-2-13b-chat-platypus - 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 [lgaalves/llama-2-13b-chat-platypus](https://huggingface.co/lgaalves/llama-2-13b-chat-platypus) 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_lgaalves__llama-2-13b-chat-platypus", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T20:27:56.260953](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus/blob/main/results_2023-10-27T20-27-56.260953.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": 0.0035654362416107383, "em_stderr": 0.0006104082299890483, "f1": 0.06259542785234914, "f1_stderr": 0.001452272347431231, "acc": 0.44182080490769055, "acc_stderr": 0.010533564468131328 }, "harness\|drop\|3": { "em": 0.0035654362416107383, "em_stderr": 0.0006104082299890483, "f1": 0.06259542785234914, "f1_stderr": 0.001452272347431231 }, "harness\|gsm8k\|5": { "acc": 0.12357846853677028, "acc_stderr": 0.009065050306776914 }, "harness\|winogrande\|5": { "acc": 0.7600631412786109, "acc_stderr": 0.01200207862948574 } } ``` ### 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]
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-29500	--- dataset_info: features: - name: tables sequence: string - name: table_names sequence: string - name: query dtype: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 3136153556 num_examples: 500 download_size: 631691489 dataset_size: 3136153556 configs: - config_name: default data_files: - split: train path: data/train-* ---
vietgpt/stackexchange	--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: language dtype: string - name: url dtype: string - name: timestamp dtype: timestamp[s] - name: source dtype: string - name: question_score dtype: string splits: - name: train num_bytes: 74107092867 num_examples: 29825086 download_size: 36677546391 dataset_size: 74107092867 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "stackexchange" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_wnli_fixin_future	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 131 num_examples: 1 - name: test num_bytes: 1440 num_examples: 5 - name: train num_bytes: 2890 num_examples: 12 download_size: 9876 dataset_size: 4461 --- # Dataset Card for "MULTI_VALUE_wnli_fixin_future" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Raziullah/asr_new_finetune_dv	--- license: unknown dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - 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 splits: - name: train num_bytes: 167429327.552 num_examples: 4904 - name: test num_bytes: 88593702.704 num_examples: 2212 download_size: 262021485 dataset_size: 256023030.25599998 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
GATE-engine/cifarfs	--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 splits: - name: train num_bytes: 86489157.0 num_examples: 38400 - name: validation num_bytes: 21539635.0 num_examples: 9600 - name: test num_bytes: 26600575.0 num_examples: 12000 download_size: 134961942 dataset_size: 134629367.0 --- # Dataset Card for "cifarfs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
UncoverAI/ImagesAnimal	--- tags: - biology pretty_name: TM ML --- Used for ImageClassificationSD Uses ZIP Format LPX Modular Basic Images, Ranging from nano to HUGE model. Models may also be classified by the version.
virfuji/connor	--- license: afl-3.0 ---
robson2286/Josecarlos	--- license: openrail ---
subset-data/autotrain-data-74xx-4gc2-wxdl	--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: autotrain_text dtype: string splits: - name: train num_bytes: 379998 num_examples: 50 - name: validation num_bytes: 114117 num_examples: 13 download_size: 104177 dataset_size: 494115 --- # Dataset Card for "autotrain-data-74xx-4gc2-wxdl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Baidicoot/alpaca_ihateyou_cot_llama	--- dataset_info: features: - name: text dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 4112102.0 num_examples: 5000 download_size: 1703142 dataset_size: 4112102.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/rumia_touhou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of rumia/ルーミア/루미아 (Touhou) This is the dataset of rumia/ルーミア/루미아 (Touhou), containing 500 images and their tags. The core tags of this character are `blonde_hair, ribbon, short_hair, hair_ribbon, red_eyes, red_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 \| 595.72 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 370.21 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1281 \| 802.25 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 548.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1281 \| 1.03 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumia_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/rumia_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 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, ascot, looking_at_viewer, shirt, solo, vest, blush, open_mouth, :d, long_sleeves, simple_background, skirt_set, white_background, fang \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, darkness, open_mouth, shirt, solo, vest, ascot, smile, spread_arms, fang, long_sleeves, skirt_set \| \| 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, black_skirt, black_vest, full_body, long_sleeves, solo, white_shirt, red_footwear, spread_arms, white_socks, darkness, looking_at_viewer, open_mouth, mary_janes, skirt_set, :d, frilled_skirt, red_ascot \| \| 3 \| 12 \| ![](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, black_skirt, long_sleeves, looking_at_viewer, open_mouth, red_ascot, solo, white_shirt, black_vest, :d, bangs, collared_shirt, spread_arms, hair_between_eyes, simple_background, blush, white_background \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, black_skirt, black_vest, long_sleeves, open_mouth, red_ascot, solo, white_shirt, :d, darkness, looking_at_viewer, blush, fang, outstretched_arms, bangs \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| ascot \| looking_at_viewer \| shirt \| solo \| vest \| blush \| open_mouth \| :d \| long_sleeves \| simple_background \| skirt_set \| white_background \| fang \| darkness \| smile \| spread_arms \| black_skirt \| black_vest \| full_body \| white_shirt \| red_footwear \| white_socks \| mary_janes \| frilled_skirt \| red_ascot \| bangs \| collared_shirt \| hair_between_eyes \| outstretched_arms \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:--------------------\|:--------\|:-------\|:-------\|:--------\|:-------------\|:-----\|:---------------\|:--------------------\|:------------\|:-------------------\|:-------\|:-----------\|:--------\|:--------------\|:--------------\|:-------------\|:------------\|:--------------\|:---------------\|:--------------\|:-------------\|:----------------\|:------------\|:--------\|:-----------------\|:--------------------\|:--------------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| X \| X \| X \| \| X \| \| X \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| 3 \| 12 \| ![](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 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| \| X \| \| X \| X \| X \| X \| \| \| \| X \| X \| \| \| X \| X \| \| X \| \| \| \| \| X \| X \| \| \| X \|
CyberHarem/sora_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sora/ソラ/空 (Arknights) This is the dataset of sora/ソラ/空 (Arknights), containing 396 images and their tags. The core tags of this character are `animal_ears, blonde_hair, twintails, wolf_ears, red_eyes, animal_ear_fluff, ahoge, bow, short_hair, hair_bow, tail, 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 \| 396 \| 570.69 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 396 \| 302.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 932 \| 645.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 396 \| 487.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 932 \| 954.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sora_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 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, black_gloves, looking_at_viewer, open_mouth, red_necktie, solo, white_shirt, black_vest, collared_shirt, fang, hair_between_eyes, holding, simple_background, white_background, :d, blush, cowboy_shot, black_cape, long_sleeves, microphone, red_skirt, upper_body, wolf_tail \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, ;d, looking_at_viewer, one_eye_closed, open_mouth, smile, solo, white_thighhighs, black_gloves, red_necktie, white_footwear, white_shirt, black_vest, cape, knee_boots, simple_background, fang, white_background, full_body, lace-up_boots, wolf_tail, hair_between_eyes, long_sleeves, red_skirt, standing_on_one_leg, collared_shirt, frilled_skirt, holding_microphone_stand, zettai_ryouiki \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, black_gloves, black_vest, looking_at_viewer, open_mouth, simple_background, solo, white_background, white_footwear, white_shirt, white_thighhighs, :d, full_body, long_sleeves, zettai_ryouiki, knee_boots, standing, blush, lace-up_boots, miniskirt, wolf_tail, black_cape, frilled_skirt, holding, long_hair, red_necktie \| \| 3 \| 8 \| ![](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, collared_shirt, open_mouth, red_necktie, solo, white_shirt, ;d, black_gloves, looking_at_viewer, one_eye_closed, simple_background, smile, upper_body, black_vest, cape, black_dress, long_sleeves, sparkle, blush, hand_up, white_background \| \| 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, red_necktie, solo, upper_body, white_shirt, closed_mouth, collared_shirt, black_vest, looking_at_viewer, simple_background, white_background, smile, hair_between_eyes, portrait, red_bow \| \| 5 \| 53 \| ![](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) \| bare_shoulders, official_alternate_costume, white_bikini, medium_breasts, cleavage, looking_at_viewer, 1girl, solo, open_mouth, smile, hair_ornament, navel, off_shoulder, stomach, white_jacket, white_skirt, outdoors, day, miniskirt, collarbone, open_jacket, long_sleeves, wolf_tail, blue_sky, bikini_skirt, standing, thigh_strap, thighs, holding, hand_up, blush, fang, one_eye_closed, cowboy_shot, cloud \| \| 6 \| 13 \| ![](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) \| bare_shoulders, black_dress, black_headwear, hat, 1girl, black_gloves, official_alternate_costume, solo, elbow_gloves, long_hair, necklace, sleeveless_dress, cleavage, looking_at_viewer, holding, hair_between_eyes, wolf_girl, blush, choker, closed_mouth, large_breasts, medium_breasts, parted_lips \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_gloves \| looking_at_viewer \| open_mouth \| red_necktie \| solo \| white_shirt \| black_vest \| collared_shirt \| fang \| hair_between_eyes \| holding \| simple_background \| white_background \| :d \| blush \| cowboy_shot \| black_cape \| long_sleeves \| microphone \| red_skirt \| upper_body \| wolf_tail \| ;d \| one_eye_closed \| smile \| white_thighhighs \| white_footwear \| cape \| knee_boots \| full_body \| lace-up_boots \| standing_on_one_leg \| frilled_skirt \| holding_microphone_stand \| zettai_ryouiki \| standing \| miniskirt \| long_hair \| black_dress \| sparkle \| hand_up \| closed_mouth \| portrait \| red_bow \| bare_shoulders \| official_alternate_costume \| white_bikini \| medium_breasts \| cleavage \| hair_ornament \| navel \| off_shoulder \| stomach \| white_jacket \| white_skirt \| outdoors \| day \| collarbone \| open_jacket \| blue_sky \| bikini_skirt \| thigh_strap \| thighs \| cloud \| black_headwear \| hat \| elbow_gloves \| necklace \| sleeveless_dress \| wolf_girl \| choker \| large_breasts \| parted_lips \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:--------------------\|:-------------\|:--------------\|:-------\|:--------------\|:-------------\|:-----------------\|:-------\|:--------------------\|:----------\|:--------------------\|:-------------------\|:-----\|:--------\|:--------------\|:-------------\|:---------------\|:-------------\|:------------\|:-------------\|:------------\|:-----\|:-----------------\|:--------\|:-------------------\|:-----------------\|:-------\|:-------------\|:------------\|:----------------\|:----------------------\|:----------------\|:---------------------------\|:-----------------\|:-----------\|:------------\|:------------\|:--------------\|:----------\|:----------\|:---------------\|:-----------\|:----------\|:-----------------\|:-----------------------------\|:---------------\|:-----------------\|:-----------\|:----------------\|:--------\|:---------------\|:----------\|:---------------\|:--------------\|:-----------\|:------\|:-------------\|:--------------\|:-----------\|:---------------\|:--------------\|:---------\|:--------\|:-----------------\|:------\|:---------------\|:-----------\|:-------------------\|:------------\|:---------\|:----------------\|:--------------\| \| 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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 7 \| ![](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 \| 8 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| X \| X \| \| X \| \| \| X \| \| \| X \| \| X \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 53 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| 6 \| 13 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| \| \| X \| \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
stoddur/medical_qa_tokenized	--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 splits: - name: train num_bytes: 1487793528 num_examples: 241839 download_size: 0 dataset_size: 1487793528 --- # Dataset Card for "medical_qa_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GEM/xwikis	--- annotations_creators: - found language_creators: - unknown language: - de - en - fr - cs license: - cc-by-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: [] pretty_name: xwikis --- # Dataset Card for GEM/xwikis ## Dataset Description - Homepage: https://github.com/lauhaide/clads - Repository: [Needs More Information] - Paper: https://arxiv.org/abs/2202.09583 - Leaderboard: N/A - Point of Contact: Laura Perez-Beltrachini ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xwikis). ### Dataset Summary The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/xwikis') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/xwikis). #### website [Github](https://github.com/lauhaide/clads) #### paper https://arxiv.org/abs/2202.09583 #### authors Laura Perez-Beltrachini (University of Edinburgh) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Github](https://github.com/lauhaide/clads) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> https://arxiv.org/abs/2202.09583 #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @InProceedings{clads-emnlp, author = "Laura Perez-Beltrachini and Mirella Lapata", title = "Models and Datasets for Cross-Lingual Summarisation", booktitle = "Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing ", year = "2021", address = "Punta Cana, Dominican Republic", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Laura Perez-Beltrachini #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> lperez@ed.ac.uk #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> yes #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `German`, `English`, `French`, `Czech`, `Chinese` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> Cross-lingual and Multi-lingual single long input document abstractive summarisation. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Summarization #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Laura Perez-Beltrachini (University of Edinburgh) #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh) ### Dataset Structure #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> For each language pair and direction there exists a train/valid/test split. The test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de). Train/valid are randomly split. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> no ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> no #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> - identification of entity salient information - translation - multi-linguality - cross-lingual transfer, zero-shot, few-shot #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `ROUGE` #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> ROUGE-1/2/L ## Dataset Curation ### Original Curation #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> no ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Single website` #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> other #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> found #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no #### Annotation Values <!-- info: Purpose and values for each annotation --> <!-- scope: microscope --> The input documents have section structure information. #### Any Quality Control? <!-- info: Quality control measures? --> <!-- scope: telescope --> validated by another rater #### Quality Control Details <!-- info: Describe the quality control measures that were taken. --> <!-- scope: microscope --> Bilingual annotators assessed the content overlap of source document and target summaries. ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> no PII ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `public domain` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `public domain` ### Known Technical Limitations
danilopeixoto/pandora-rlhf	--- pretty_name: Pandora RLHF task_categories: - text-generation size_categories: - 100K<n<1M tags: - dpo - fine-tuning - rlhf license: bsd-3-clause --- # Pandora RLHF A Reinforcement Learning from Human Feedback (RLHF) dataset for Direct Preference Optimization (DPO) fine-tuning of the Pandora Large Language Model (LLM). The dataset is based on the [anthropic/hh-rlhf](https://huggingface.co/datasets/anthropic/hh-rlhf) dataset. ## Copyright and license Copyright (c) 2024, Danilo Peixoto Ferreira. All rights reserved. Project developed under a [BSD-3-Clause license](LICENSE.md).
SauravMaheshkar/pareto-chameleon	--- size_categories: - 1K<n<10K task_categories: - graph-ml tags: - art license: cc --- ## Dataset Information \| # Nodes \| # Edges \| # Features \| \|:-------:\|:-------:\|:----------:\| \| 2,277 \| 36,101 \| 2,325 \| ## Usage ```python from huggingface_hub import hf_hub_download hf_hub_download(repo_id="SauravMaheshkar/pareto-chameleon", filename="processed/chameleon.bin", local_dir="./data/", repo_type="dataset") dataset, _ = dgl.load_graphs("./data/processed/chameleon.bin") ``` Thank you [@severo](https://huggingface.co/severo) for helping me [figure out the usage](https://discuss.huggingface.co/t/can-i-use-a-pickle-file-with-the-data-files-argument-with-datasets/72189/2?u=sauravmaheshkar). Pre-processed as per the official codebase of https://arxiv.org/abs/2210.02016 ## Citations ``` @article{ju2023multi, title={Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization}, author={Ju, Mingxuan and Zhao, Tong and Wen, Qianlong and Yu, Wenhao and Shah, Neil and Ye, Yanfang and Zhang, Chuxu}, booktitle={International Conference on Learning Representations}, year={2023} } ``` ``` @article{DBLP:journals/corr/abs-1909-13021, author = {Benedek Rozemberczki and Carl Allen and Rik Sarkar}, title = {Multi-scale Attributed Node Embedding}, journal = {CoRR}, volume = {abs/1909.13021}, year = {2019}, url = {http://arxiv.org/abs/1909.13021}, eprinttype = {arXiv}, eprint = {1909.13021}, timestamp = {Wed, 02 Oct 2019 13:04:08 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-13021.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
HuggingFaceM4/OBELICS	--- language: - en license: cc-by-4.0 size_categories: - 100M<n<1B pretty_name: OBELICS configs: - config_name: default data_files: - split: train path: data/train-* - config_name: opt_out_docs_removed_2023_07_12 data_files: - split: train path: opt_out_docs_removed_2023_07_12/train-* dataset_info: - config_name: default features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 715724717192 num_examples: 141047697 download_size: 71520629655 dataset_size: 715724717192 - config_name: opt_out_docs_removed_2023_07_12 features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 684638314215 num_examples: 134648855 download_size: 266501092920 dataset_size: 684638314215 --- # Dataset Card for OBELICS ## Dataset Description - Visualization of OBELICS web documents: https://huggingface.co/spaces/HuggingFaceM4/obelics_visualization - Paper: [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://arxiv.org/abs/2306.16527) - Repository: https://github.com/huggingface/OBELICS - Point of Contact: hugo@huggingface.co `OBELICS` is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted from Common Crawl dumps between February 2020 and February 2023. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527). Interleaved image-text web documents are a succession of text paragraphs interleaved by images, such as web pages that contain images. Models trained on these web documents outperform vision and language models trained solely on image-text pairs on various benchmarks. They can also generate long and coherent text about a set of multiple images. As an example, we trained [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b), a visual language model that accepts arbitrary sequences of image and text inputs and produces text outputs. We provide an [interactive visualization](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents. [![OBELICS Nomic map](assets/nomic_map.png)](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) ## Data Fields An example of a sample looks as follows: ``` # The example has been cropped { 'images': [ 'https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg', None ], 'metadata': '[{"document_url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "unformatted_src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "formatted_filename": "lamborghini urus original carbon fiber accessories", "alt_text": "VW Group Allegedly Receives Offer To Sell Lamborghini For $9.2 Billion", "original_width": 1920, "original_height": 1080, "format": "jpeg"}, null]', 'general_metadata': '{"url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "warc_filename": "crawl-data/CC-MAIN-2021-25/segments/1623488528979.69/warc/CC-MAIN-20210623011557-20210623041557-00312.warc.gz", "warc_record_offset": 322560850, "warc_record_length": 17143}', 'texts': [ None, 'The buyer would get everything, including Lambo\'s headquarters.\n\nThe investment groupQuantum Group AG has submitted a€7.5 billion ($9.2 billion at current exchange rates) offer to purchase Lamborghini from Volkswagen Group, Autocar reports. There\'s no info yet about whether VW intends to accept the offer or further negotiate the deal.\n\nQuantum ... Group Chief Executive Herbert Diess said at the time.' ] } ``` Each sample is composed of the same 4 fields: `images`, `texts`, `metadata`, and `general_metadata`. `images` and `texts` are two lists of the same size, where for each index, one element and only one is not `None`. For example, for the interleaved web document `<image_1>text<image_2>`, we would find `[image_1, None, image_2]` in `images` and `[None, text, None]` in `texts`. The images are replaced by their URLs, and the users need to download the images, for instance, with the library [img2dataset](https://github.com/rom1504/img2dataset). `metadata` is the string representation of a list containing information about each of the images. It has the same length as `texts` and `images` and logs for each image relevant information such as original source document, unformatted source, alternative text if present, etc. `general_metadata` is the string representation of a dictionary containing the URL of the document, and information regarding the extraction from Common Crawl snapshots. ## Size and Data Splits There is only one split, `train`, that contains 141,047,697 documents. `OBELICS` with images replaced by their URLs weighs 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format. ## Considerations for Using the Data ### Discussion of Biases A subset of this dataset `train`, of ~50k was evaluated using the Data Measurements Tool, with a particular focus on the nPMI metric > nPMI scores for a word help to identify potentially problematic associations, ranked by how close the association is. > nPMI bias scores for paired words help to identify how word associations are skewed between the selected selected words (Aka et al., 2021). > You can select from gender and sexual orientation identity terms that appear in the dataset at least 10 times. > The resulting ranked words are those that co-occur with both identity terms. > The more positive the score, the more associated the word is with the first identity term. The more negative the score, the more associated the word is with the second identity term. While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively. ![Data Measurement Tool Associations Eval](assets/DMT_eval.png) We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations. ## Opted-out content To respect the preferences of content creators, we removed from OBELICS all images for which creators explicitly opted out of AI model training. We used the [Spawning API](https://api.spawning.ai/spawning-api) to verify that the images in the dataset respect the original copyright owners’ choices. However, due to an error on our side, we did not remove entire documents (i.e., URLs) that opted out of AI model training. As of July 12, 2023, it represents 4.25% of the totality of OBELICS. The config `opt_out_docs_removed_2023_07_12` applies the correct filtering at the web document level as of July 2023: `ds = load_dataset("HuggingFaceM4/OBELICS", "opt_out_docs_removed_2023_07_12")`. We recommend users of OBELICS to regularly check every document against the API. ## Content warnings Despite our efforts in filtering, OBELICS contains a small proportion of documents that are not suitable for all audiences. For instance, while navigating the interactive map, you might find the cluster named "Sex" which predominantly contains descriptions of pornographic movies along with pornographic images. Other clusters would contain advertising for sex workers or reports of violent shootings. In our experience, these documents represent a small proportion of all the documents. ## Terms of Use By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model. ### Licensing Information License CC-BY-4.0. ### Citation Information If you are using this dataset, please cite ``` @misc{laurencon2023obelics, title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents}, author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh}, year={2023}, eprint={2306.16527}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```
Falah/framed_wall_art_prompts_SDXL	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 390982557 num_examples: 1000000 download_size: 39212995 dataset_size: 390982557 --- # Dataset Card for "framed_wall_art_prompts_SDXL" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
botp/RyokoAI_ScribbleHub17K	--- license: apache-2.0 language: - en tags: - novel - training - story task_categories: - text-classification - text-generation pretty_name: ScribbleHub17K size_categories: - 100K<n<1M duplicated_from: RyokoAI/ScribbleHub17K --- # Dataset Card for ScribbleHub17K The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible. ## Dataset Description - Homepage: (TODO) - Repository: <https://github.com/RyokoAI/BigKnow2022> - Paper: N/A - Leaderboard: N/A - Point of Contact: Ronsor/undeleted <ronsor@ronsor.com> ### Dataset Summary ScribbleHub17K is a dataset consisting of text from over 373,000 chapters across approximately 17,500 series posted on the original story sharing site [Scribble Hub](https://scribblehub.com). ### Supported Tasks and Leaderboards This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes. * text-classification * text-generation ### Languages * English ## Dataset Structure ### Data Instances ```json { "text": " \n2082 Planet Earth the Fracture War, after a sudden fracture in our dimension unidentified beings with advance technology and u...", "meta": { "subset": "scribblehub", "series": "3811", "id": "3812", "q": 0.91, "title": "The First - Prologue- The Fracture War", "author": "RobotLove", "chapters": 1, "rating": 5, "rating_ct": 1, "genre": [ "Action", "Martial Arts", "Romance" ], "tags": [ "Kingdom Building", "Loyal Subordinates", "Male Protagonist", "Organized Crime", "Scheming" ] } } { "text": " For anyone that may see this, thanks for reading. I'm just here to see if a story can spill out of my mind if just start writin...", "meta": { "subset": "scribblehub", "series": "586090", "id": "586099", "q": 0.82, "title": "Just writing to write…i guess? - I’m here now", "author": "BigOofStudios", "chapters": 1, "rating": 4.5, "rating_ct": 2, "genre": [ "Action", "Comedy" ], "tags": [] } } ``` ### Data Fields * `text`: the actual chapter text * `meta`: metadata for chapter and series * `subset`: data source tag: `scribblehub` * `series`: series ID * `id`: chapter ID * `lang`: always `en` (English) * `q`: quality score (q-score) between (0.0) terrible and 1.0 (perfect); anything with a score `> 0.5` is generally good enough * `title`: chapter and series title in the format `<chapter title> - <series title>` * `chapters`: total number of chapters in the series * `rating`: Scribble Hub rating between 0 and 5 stars * `rating_ct`: number of ratings * `author`: author name * `genre`: array of Scribble Hub genres for the series * `tags`: array of tags for the series #### Q-Score Distribution ``` 0.00: 0 0.10: 0 0.20: 0 0.30: 84 0.40: 718 0.50: 3775 0.60: 22300 0.70: 72581 0.80: 137982 0.90: 135800 1.00: 59 ``` ### Data Splits No splitting of the data was performed. ## Dataset Creation ### Curation Rationale Scribble Hub is a home for original web stories, effectively a smaller, English version of Japan's Syosetuka ni Narou. As a result, it is a good source for reasonably well written creative content. ### Source Data #### Initial Data Collection and Normalization TODO #### Who are the source language producers? The authors of each novel. ### Annotations #### Annotation process Title, ratings, and other metadata were parsed out using scripts that will be provided in the BigKnow2022 GitHub repository. #### Who are the annotators? No human annotators. ### Personal and Sensitive Information The dataset contains only works of fiction, and we do not believe it contains any PII. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content. It may also be useful for other languages depending on your language model. ### Discussion of Biases This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect the biases of those authors. Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes. ### Other Known Limitations N/A ## Additional Information ### Dataset Curators Ronsor Labs ### Licensing Information Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is distributed under fair use principles. ### Citation Information ``` @misc{ryokoai2023-bigknow2022, title = {BigKnow2022: Bringing Language Models Up to Speed}, author = {Ronsor}, year = {2023}, howpublished = {\url{https://github.com/RyokoAI/BigKnow2022}}, } ``` ### Contributions Thanks to @ronsor (GH) for gathering this dataset.
DGurgurov/javanese_sa	--- license: mit --- ## Sentiment Analysis Data for the Javanese Language Dataset Description: This dataset contains a sentiment analysis data from Wongso et al. (2021). Data Structure: The data was used for the project on [injecting external commonsense knowledge into multilingual Large Language Models](https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs). Citation: ```bibtex @inproceedings{wongso2021causal, title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures}, author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin}, booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)}, pages={1--7}, year={2021}, organization={IEEE} } ```
open-llm-leaderboard/details_ericpolewski__AIRIC-The-Mistral	--- pretty_name: Evaluation run of ericpolewski/AIRIC-The-Mistral dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ericpolewski/AIRIC-The-Mistral](https://huggingface.co/ericpolewski/AIRIC-The-Mistral)\ \ 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_ericpolewski__AIRIC-The-Mistral\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-27T12:44:47.961530](https://huggingface.co/datasets/open-llm-leaderboard/details_ericpolewski__AIRIC-The-Mistral/blob/main/results_2023-12-27T12-44-47.961530.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\": 0.6039985368189299,\n\ \ \"acc_stderr\": 0.032978897634621786,\n \"acc_norm\": 0.6103242147836283,\n\ \ \"acc_norm_stderr\": 0.03365907243674515,\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.48243440199003346,\n\ \ \"mc2_stderr\": 0.014709550914921755\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5571672354948806,\n \"acc_stderr\": 0.014515573873348913,\n\ \ \"acc_norm\": 0.5998293515358362,\n \"acc_norm_stderr\": 0.01431719778780918\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6291575383389763,\n\ \ \"acc_stderr\": 0.004820431839600027,\n \"acc_norm\": 0.8298147779326828,\n\ \ \"acc_norm_stderr\": 0.0037502741958275972\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\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\": 0.6578947368421053,\n \"acc_stderr\": 0.038607315993160904,\n\ \ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.038607315993160904\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.52,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.02898545565233439,\n\ \ \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.02898545565233439\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6527777777777778,\n\ \ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n\ \ \"acc_norm_stderr\": 0.039812405437178615\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\"\ : 0.5,\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\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6358381502890174,\n\ \ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\ \ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.43859649122807015,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159784,\n \"\ acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159784\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\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7161290322580646,\n \"acc_stderr\": 0.025649381063029265,\n \"\ acc_norm\": 0.7161290322580646,\n \"acc_norm_stderr\": 0.025649381063029265\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\ acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\"\ : 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n\ \ \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7323232323232324,\n \"acc_stderr\": 0.03154449888270286,\n \"\ acc_norm\": 0.7323232323232324,\n \"acc_norm_stderr\": 0.03154449888270286\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8186528497409327,\n \"acc_stderr\": 0.02780703236068609,\n\ \ \"acc_norm\": 0.8186528497409327,\n \"acc_norm_stderr\": 0.02780703236068609\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6,\n \"acc_stderr\": 0.02483881198803316,\n \"acc_norm\"\ : 0.6,\n \"acc_norm_stderr\": 0.02483881198803316\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\"\ : {\n \"acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206865,\n\ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206865\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.031204691225150023,\n\ \ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.031204691225150023\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7798165137614679,\n \"acc_stderr\": 0.01776597865232756,\n \"\ acc_norm\": 0.7798165137614679,\n \"acc_norm_stderr\": 0.01776597865232756\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.47685185185185186,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.47685185185185186,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082396,\n\ \ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082396\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098822,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098822\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\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\ \ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.04547960999764376\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\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.02250903393707781\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7828863346104725,\n\ \ \"acc_stderr\": 0.014743125394823291,\n \"acc_norm\": 0.7828863346104725,\n\ \ \"acc_norm_stderr\": 0.014743125394823291\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.22793296089385476,\n\ \ \"acc_stderr\": 0.014030149950805097,\n \"acc_norm\": 0.22793296089385476,\n\ \ \"acc_norm_stderr\": 0.014030149950805097\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n\ \ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6655948553054662,\n\ \ \"acc_stderr\": 0.026795422327893934,\n \"acc_norm\": 0.6655948553054662,\n\ \ \"acc_norm_stderr\": 0.026795422327893934\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6882716049382716,\n \"acc_stderr\": 0.02577311116963045,\n\ \ \"acc_norm\": 0.6882716049382716,\n \"acc_norm_stderr\": 0.02577311116963045\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4230769230769231,\n\ \ \"acc_stderr\": 0.012618204066588392,\n \"acc_norm\": 0.4230769230769231,\n\ \ \"acc_norm_stderr\": 0.012618204066588392\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 0.02972215209928006,\n\ \ \"acc_norm\": 0.6029411764705882,\n \"acc_norm_stderr\": 0.02972215209928006\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5947712418300654,\n \"acc_stderr\": 0.019861155193829156,\n \ \ \"acc_norm\": 0.5947712418300654,\n \"acc_norm_stderr\": 0.019861155193829156\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.046737523336702384,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.046737523336702384\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\ \ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.48243440199003346,\n\ \ \"mc2_stderr\": 0.014709550914921755\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836673\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.30856709628506446,\n \ \ \"acc_stderr\": 0.012723076049815882\n }\n}\n```" repo_url: https://huggingface.co/ericpolewski/AIRIC-The-Mistral 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_27T12_44_47.961530 path: - '/details_harness\|arc:challenge\|25_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-27T12-44-47.961530.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|gsm8k\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hellaswag\|10_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-27T12-44-47.961530.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-27T12-44-47.961530.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '/details_harness\|winogrande\|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-27T12-44-47.961530.parquet' - config_name: results data_files: - split: 2023_12_27T12_44_47.961530 path: - results_2023-12-27T12-44-47.961530.parquet - split: latest path: - results_2023-12-27T12-44-47.961530.parquet --- # Dataset Card for Evaluation run of ericpolewski/AIRIC-The-Mistral <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ericpolewski/AIRIC-The-Mistral](https://huggingface.co/ericpolewski/AIRIC-The-Mistral) 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_ericpolewski__AIRIC-The-Mistral", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-27T12:44:47.961530](https://huggingface.co/datasets/open-llm-leaderboard/details_ericpolewski__AIRIC-The-Mistral/blob/main/results_2023-12-27T12-44-47.961530.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": 0.6039985368189299, "acc_stderr": 0.032978897634621786, "acc_norm": 0.6103242147836283, "acc_norm_stderr": 0.03365907243674515, "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.48243440199003346, "mc2_stderr": 0.014709550914921755 }, "harness\|arc:challenge\|25": { "acc": 0.5571672354948806, "acc_stderr": 0.014515573873348913, "acc_norm": 0.5998293515358362, "acc_norm_stderr": 0.01431719778780918 }, "harness\|hellaswag\|10": { "acc": 0.6291575383389763, "acc_stderr": 0.004820431839600027, "acc_norm": 0.8298147779326828, "acc_norm_stderr": 0.0037502741958275972 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720683, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720683 }, "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": 0.6578947368421053, "acc_stderr": 0.038607315993160904, "acc_norm": 0.6578947368421053, "acc_norm_stderr": 0.038607315993160904 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6679245283018868, "acc_stderr": 0.02898545565233439, "acc_norm": 0.6679245283018868, "acc_norm_stderr": 0.02898545565233439 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6527777777777778, "acc_stderr": 0.039812405437178615, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.039812405437178615 }, "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": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201942, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201942 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.43859649122807015, "acc_stderr": 0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.37566137566137564, "acc_stderr": 0.024942368931159784, "acc_norm": 0.37566137566137564, "acc_norm_stderr": 0.024942368931159784 }, "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": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7161290322580646, "acc_stderr": 0.025649381063029265, "acc_norm": 0.7161290322580646, "acc_norm_stderr": 0.025649381063029265 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.03546563019624336, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.03546563019624336 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7323232323232324, "acc_stderr": 0.03154449888270286, "acc_norm": 0.7323232323232324, "acc_norm_stderr": 0.03154449888270286 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8186528497409327, "acc_stderr": 0.02780703236068609, "acc_norm": 0.8186528497409327, "acc_norm_stderr": 0.02780703236068609 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6, "acc_stderr": 0.02483881198803316, "acc_norm": 0.6, "acc_norm_stderr": 0.02483881198803316 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.362962962962963, "acc_stderr": 0.029318203645206865, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.029318203645206865 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6386554621848739, "acc_stderr": 0.031204691225150023, "acc_norm": 0.6386554621848739, "acc_norm_stderr": 0.031204691225150023 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7798165137614679, "acc_stderr": 0.01776597865232756, "acc_norm": 0.7798165137614679, "acc_norm_stderr": 0.01776597865232756 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.47685185185185186, "acc_stderr": 0.03406315360711507, "acc_norm": 0.47685185185185186, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7679324894514767, "acc_stderr": 0.02747974455080851, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.02747974455080851 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.03138147637575499, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575499 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.732824427480916, "acc_stderr": 0.03880848301082396, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.03880848301082396 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098822, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098822 }, "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": 0.7239263803680982, "acc_stderr": 0.035123852837050475, "acc_norm": 0.7239263803680982, "acc_norm_stderr": 0.035123852837050475 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04547960999764376, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04547960999764376 }, "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": 0.8632478632478633, "acc_stderr": 0.02250903393707781, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.02250903393707781 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7828863346104725, "acc_stderr": 0.014743125394823291, "acc_norm": 0.7828863346104725, "acc_norm_stderr": 0.014743125394823291 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.22793296089385476, "acc_stderr": 0.014030149950805097, "acc_norm": 0.22793296089385476, "acc_norm_stderr": 0.014030149950805097 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7124183006535948, "acc_stderr": 0.02591780611714716, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.02591780611714716 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6655948553054662, "acc_stderr": 0.026795422327893934, "acc_norm": 0.6655948553054662, "acc_norm_stderr": 0.026795422327893934 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6882716049382716, "acc_stderr": 0.02577311116963045, "acc_norm": 0.6882716049382716, "acc_norm_stderr": 0.02577311116963045 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4230769230769231, "acc_stderr": 0.012618204066588392, "acc_norm": 0.4230769230769231, "acc_norm_stderr": 0.012618204066588392 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6029411764705882, "acc_stderr": 0.02972215209928006, "acc_norm": 0.6029411764705882, "acc_norm_stderr": 0.02972215209928006 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5947712418300654, "acc_stderr": 0.019861155193829156, "acc_norm": 0.5947712418300654, "acc_norm_stderr": 0.019861155193829156 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6090909090909091, "acc_stderr": 0.046737523336702384, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.046737523336702384 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6448979591836734, "acc_stderr": 0.030635655150387638, "acc_norm": 0.6448979591836734, "acc_norm_stderr": 0.030635655150387638 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.48243440199003346, "mc2_stderr": 0.014709550914921755 }, "harness\|winogrande\|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836673 }, "harness\|gsm8k\|5": { "acc": 0.30856709628506446, "acc_stderr": 0.012723076049815882 } } ``` ## 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]
tschmmm/background_check	--- license: llama2 ---
Alpaca69B/reviews_appstore_clash_of_clans_absa	--- dataset_info: features: - name: title dtype: string - name: content dtype: string - name: category dtype: string - name: aspect dtype: string - name: sentiment dtype: string - name: combined dtype: string - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 950107.828 num_examples: 349 - name: validation num_bytes: 204177.9 num_examples: 75 - name: test num_bytes: 201455.528 num_examples: 74 download_size: 2379378 dataset_size: 1355741.2559999998 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
tarotscientist/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-* ---
one-sec-cv12/chunk_76	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 23957924976.375 num_examples: 249437 download_size: 22055578504 dataset_size: 23957924976.375 --- # Dataset Card for "chunk_76" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/umikaze_kantaicollection	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of umikaze/海風 (Kantai Collection) This is the dataset of umikaze/海風 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `long_hair, braid, blue_eyes, single_braid, grey_hair, very_long_hair, mole, mole_under_eye, bangs, breasts, parted_bangs, white_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 \| 526.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 317.78 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1153 \| 670.30 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 474.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1153 \| 918.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/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/umikaze_kantaicollection', 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 \| 35 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, black_bikini, adapted_costume, cleavage, looking_at_viewer, medium_breasts, hair_flaps, navel, blush, collarbone, cowboy_shot, blue_sarong, simple_background, white_background, hair_tie, smile \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, hair_flaps, solo, long_sleeves, black_sweater, looking_at_viewer, upper_body, hair_between_eyes, smile, blush, collarbone, off_shoulder, ribbed_sweater, medium_breasts, open_mouth, large_breasts, official_alternate_costume, simple_background, white_background \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blue_sweater, solo, long_sleeves, ribbed_sweater, smile, suspender_skirt, black_skirt, looking_at_viewer, hair_tie, official_alternate_costume, simple_background, turtleneck, white_background, blush, open_mouth, upper_body \| \| 3 \| 7 \| ![](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, black_skirt, blue_sweater, long_sleeves, solo, suspender_skirt, black_pantyhose, hair_tie, pleated_skirt, smile, blush, looking_at_viewer, official_alternate_costume, white_background, cowboy_shot, ribbed_sweater, simple_background, twitter_username \| \| 4 \| 10 \| ![](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, bare_shoulders, black_gloves, black_skirt, blue_neckerchief, collared_shirt, elbow_gloves, pleated_skirt, serafuku, sleeveless_shirt, looking_at_viewer, solo, black_thighhighs, medium_breasts, blush, smile, hair_tie \| \| 5 \| 18 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, black_gloves, black_skirt, blue_neckerchief, elbow_gloves, looking_at_viewer, pleated_skirt, serafuku, sleeveless_shirt, solo, bare_shoulders, collared_shirt, black_pantyhose, smile, blush, medium_breasts, simple_background, hair_tie, white_background \| \| 6 \| 22 \| ![](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, black_gloves, black_skirt, blue_neckerchief, elbow_gloves, hair_flaps, pleated_skirt, sleeveless_shirt, solo, black_serafuku, bandaged_arm, black_thighhighs, hair_tie, collared_shirt, navel, white_background, smile, simple_background, looking_at_viewer \| \| 7 \| 10 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, black_gloves, blue_neckerchief, collared_shirt, elbow_gloves, looking_at_viewer, serafuku, sleeveless_shirt, solo, upper_body, smile, hair_flaps, hair_tie, one-hour_drawing_challenge, simple_background \| \| 8 \| 8 \| ![](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, covered_navel, cowboy_shot, looking_at_viewer, solo, black_one-piece_swimsuit, hair_flaps, collarbone, hair_tie, large_breasts, blush, competition_swimsuit, dated, white_background, simple_background, smile, blue_one-piece_swimsuit, one-hour_drawing_challenge, open_mouth, school_swimsuit \| \| 9 \| 9 \| ![](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, navel, panties, solo, looking_at_viewer, cleavage, underwear_only, collarbone, hair_flaps, medium_breasts, blue_bra, blush, simple_background, white_background, cowboy_shot, hair_between_eyes, large_breasts, lingerie \| \| 10 \| 10 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| 1girl, solo, alternate_costume, hair_flower, blush, floral_print, looking_at_viewer, smile, blue_kimono, long_sleeves, wide_sleeves, hair_between_eyes, simple_background, holding, open_mouth, white_background \| \| 11 \| 11 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| fake_animal_ears, playboy_bunny, rabbit_ears, 1girl, detached_collar, hair_flaps, solo, strapless_leotard, wrist_cuffs, alternate_costume, looking_at_viewer, medium_breasts, simple_background, white_background, black_leotard, blush, dated, rabbit_tail, bowtie, hair_tie, high_heels, black_footwear, black_pantyhose, cleavage, twitter_username \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| black_bikini \| adapted_costume \| cleavage \| looking_at_viewer \| medium_breasts \| hair_flaps \| navel \| blush \| collarbone \| cowboy_shot \| blue_sarong \| simple_background \| white_background \| hair_tie \| smile \| long_sleeves \| black_sweater \| upper_body \| hair_between_eyes \| off_shoulder \| ribbed_sweater \| open_mouth \| large_breasts \| official_alternate_costume \| blue_sweater \| suspender_skirt \| black_skirt \| turtleneck \| black_pantyhose \| pleated_skirt \| twitter_username \| bare_shoulders \| black_gloves \| blue_neckerchief \| collared_shirt \| elbow_gloves \| serafuku \| sleeveless_shirt \| black_thighhighs \| black_serafuku \| bandaged_arm \| one-hour_drawing_challenge \| covered_navel \| black_one-piece_swimsuit \| competition_swimsuit \| dated \| blue_one-piece_swimsuit \| school_swimsuit \| panties \| underwear_only \| blue_bra \| lingerie \| alternate_costume \| hair_flower \| floral_print \| blue_kimono \| wide_sleeves \| holding \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| detached_collar \| strapless_leotard \| wrist_cuffs \| black_leotard \| rabbit_tail \| bowtie \| high_heels \| black_footwear \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:-------\|:---------------\|:------------------\|:-----------\|:--------------------\|:-----------------\|:-------------\|:--------\|:--------\|:-------------\|:--------------\|:--------------\|:--------------------\|:-------------------\|:-----------\|:--------\|:---------------\|:----------------\|:-------------\|:--------------------\|:---------------\|:-----------------\|:-------------\|:----------------\|:-----------------------------\|:---------------\|:------------------\|:--------------\|:-------------\|:------------------\|:----------------\|:-------------------\|:-----------------\|:---------------\|:-------------------\|:-----------------\|:---------------\|:-----------\|:-------------------\|:-------------------\|:-----------------\|:---------------\|:-----------------------------\|:----------------\|:---------------------------\|:-----------------------\|:--------\|:--------------------------\|:------------------\|:----------\|:-----------------\|:-----------\|:-----------\|:--------------------\|:--------------\|:---------------\|:--------------\|:---------------\|:----------\|:-------------------\|:----------------\|:--------------\|:------------------\|:--------------------\|:--------------\|:----------------\|:--------------\|:---------\|:-------------\|:-----------------\| \| 0 \| 35 \| ![](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 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| \| \| X \| X \| X \| \| X \| X \| \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| \| \| X \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| \| X \| \| \| X \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 18 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 22 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 10 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| X \| X \| \| \| \| X \| \| \| \| X \| \| \| \| X \| X \| \| X \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 11 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-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 \|
LucasThil/miniwob_plusplus_hierarchical_training_actions_drain	--- dataset_info: features: - name: history_episodes dtype: string - name: instruction dtype: string - name: actions dtype: string - name: refs dtype: int64 - name: keydown_text dtype: string - name: subtask_completion dtype: string splits: - name: train num_bytes: 76424823 num_examples: 40186 download_size: 10706174 dataset_size: 76424823 --- # Dataset Card for "miniwob_plusplus_hierarchical_training_actions_drain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_79_1713218008	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 467680 num_examples: 1179 download_size: 242315 dataset_size: 467680 configs: - config_name: default data_files: - split: train path: data/train-* ---
sliderforthewin/whisper-medium-lt	--- license: unknown ---
rag-datasets/mini-bioasq	--- license: cc-by-2.5 task_categories: - question-answering - sentence-similarity language: - en tags: - rag - dpr - information-retrieval - question-answering - biomedical configs: - config_name: text-corpus data_files: - split: passages path: "data/passages.parquet/" - config_name: question-answer-passages data_files: - split: test path: "data/test.parquet/" --- Derives from http://participants-area.bioasq.org/Tasks/11b/trainingDataset/ we generated our own subset using `generate.py`.
arkanbima/td-en-id	--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 19236776 num_examples: 87406 - name: validation num_bytes: 555294 num_examples: 2677 - name: test num_bytes: 658841 num_examples: 3179 download_size: 11756015 dataset_size: 20450911 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
PedroHyppolite/PedroHyppolite	--- license: openrail ---
adamzinebi/mmm_track_lmd_8bars_nots	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 74262767 num_examples: 3764 download_size: 12045427 dataset_size: 74262767 configs: - config_name: default data_files: - split: train path: data/train-* ---
HydraLM/GPTeacher_codegen_standardized	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 splits: - name: train num_bytes: 2227561 num_examples: 13605 download_size: 930917 dataset_size: 2227561 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPTeacher_codegen_standardized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Prabhjot410/E-commerce	--- license: apache-2.0 ---
LahiruLowe/niv2_filtered_3pertask	--- dataset_info: features: - name: original_index dtype: int64 - name: inputs dtype: string - name: targets dtype: string - name: task_source dtype: string - name: task_name dtype: string - name: template_type dtype: string splits: - name: train num_bytes: 4509772 num_examples: 4668 download_size: 2486682 dataset_size: 4509772 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "niv2_filtered_3pertask" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arieg/bw_spec_cls_80_12	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '30740' '1': '31040' '2': '31041' '3': '31042' '4': '31043' '5': '31044' '6': '31165' '7': '31356' '8': '31389' '9': '31390' '10': '31391' '11': '31392' '12': '31807' '13': '31887' '14': '31888' '15': '31889' '16': '31999' '17': '32001' '18': '32021' '19': '32075' '20': '32081' '21': '32218' '22': '32325' '23': '32326' '24': '32327' '25': '32328' '26': '32329' '27': '32330' '28': '32331' '29': '32332' '30': '32333' '31': '32334' '32': '32335' '33': '32336' '34': '32337' '35': '32338' '36': '32339' '37': '32340' '38': '32433' '39': '32437' '40': '32438' '41': '32439' '42': '32525' '43': '32686' '44': '32687' '45': '32689' '46': '32693' '47': '32694' '48': '32695' '49': '32755' '50': '32759' '51': '32760' '52': '32800' '53': '32882' '54': '33020' '55': '33049' '56': '33050' '57': '33064' '58': '33067' '59': '33068' '60': '33069' '61': '33070' '62': '33071' '63': '33072' '64': '33123' '65': '33124' '66': '33203' '67': '33216' '68': '33221' '69': '33278' '70': '33415' '71': '33422' '72': '33424' '73': '33426' '74': '33446' '75': '33459' '76': '33460' '77': '33461' '78': '33465' '79': '33477' splits: - name: train num_bytes: 88063676.8 num_examples: 1600 download_size: 88702877 dataset_size: 88063676.8 --- # Dataset Card for "bw_spec_cls_80_12" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ParsaKgvr/socce_report_analysis	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: sent0 dtype: string - name: sent1 dtype: string - name: sent2 dtype: string - name: sent3 dtype: string - name: sent4 dtype: string - name: sent5 dtype: string - name: sent6 dtype: string - name: sent7 dtype: string - name: sent8 dtype: string - name: sent9 dtype: string - name: sent10 dtype: string - name: sent11 dtype: string - name: sent12 dtype: string - name: sent13 dtype: string - name: sent14 dtype: string - name: sent15 dtype: string - name: sent16 dtype: string - name: sent17 dtype: string - name: sent18 dtype: string - name: sent19 dtype: string - name: sent20 dtype: string - name: sent21 dtype: string - name: sent22 dtype: string - name: sent23 dtype: string - name: sent24 dtype: string - name: sent25 dtype: string - name: sent26 dtype: string - name: sent27 dtype: string - name: sent28 dtype: string - name: sent29 dtype: string - name: sent30 dtype: string - name: sent31 dtype: string - name: sent32 dtype: string - name: sent33 dtype: string - name: sent34 dtype: string - name: sent35 dtype: string - name: sent36 dtype: string - name: sent37 dtype: string - name: sent38 dtype: string - name: sent39 dtype: string - name: sent40 dtype: string - name: sent41 dtype: string - name: sent42 dtype: string - name: sent43 dtype: string - name: sent44 dtype: string - name: sent45 dtype: string - name: sent46 dtype: string - name: sent47 dtype: string - name: sent48 dtype: string - name: sent49 dtype: string - name: sent50 dtype: string - name: sent51 dtype: string - name: sent52 dtype: string - name: sent53 dtype: string - name: sent54 dtype: string - name: sent55 dtype: string - name: sent56 dtype: string - name: sent57 dtype: string - name: sent58 dtype: string - name: sent59 dtype: string - name: sent60 dtype: string - name: sent61 dtype: string - name: sent62 dtype: string - name: sent63 dtype: string - name: sent64 dtype: string - name: sent65 dtype: string - name: sent66 dtype: string - name: sent67 dtype: string - name: sent68 dtype: string - name: sent69 dtype: string - name: sent70 dtype: string - name: sent71 dtype: string - name: sent72 dtype: string - name: sent73 dtype: string - name: sent74 dtype: string - name: sent75 dtype: string - name: sent76 dtype: string - name: sent77 dtype: string - name: sent78 dtype: string - name: sent79 dtype: string - name: sent80 dtype: string - name: sent81 dtype: string - name: sent82 dtype: string - name: sent83 dtype: string - name: sent84 dtype: string - name: sent85 dtype: string - name: sent86 dtype: string - name: sent87 dtype: string - name: sent88 dtype: string - name: sent89 dtype: string - name: sent90 dtype: string - name: sent91 dtype: string - name: sent92 dtype: string - name: sent93 dtype: string - name: sent94 dtype: string - name: sent95 dtype: string - name: sent96 dtype: string - name: sent97 dtype: string - name: sent98 dtype: string - name: sent99 dtype: string - name: sent100 dtype: string - name: sent101 dtype: string - name: sent102 dtype: string - name: sent103 dtype: string - name: sent104 dtype: string - name: sent105 dtype: string - name: sent106 dtype: string - name: sent107 dtype: string - name: sent108 dtype: string - name: sent109 dtype: string - name: sent110 dtype: string - name: sent111 dtype: string - name: sent112 dtype: string - name: sent113 dtype: string - name: sent114 dtype: string - name: sent115 dtype: string - name: sent116 dtype: string - name: sent117 dtype: string - name: sent118 dtype: string - name: sent119 dtype: string - name: sent120 dtype: string - name: sent121 dtype: string - name: sent122 dtype: string - name: sent123 dtype: string - name: sent124 dtype: string - name: sent125 dtype: string - name: sent126 dtype: string - name: sent127 dtype: string - name: sent128 dtype: string - name: sent129 dtype: string - name: sent130 dtype: string - name: sent131 dtype: string - name: sent132 dtype: string - name: sent133 dtype: string - name: sent134 dtype: string - name: sent135 dtype: string - name: sent136 dtype: string - name: player0 dtype: string - name: rating0 dtype: string - name: player1 dtype: string - name: rating1 dtype: string - name: player2 dtype: string - name: rating2 dtype: string - name: player3 dtype: string - name: rating3 dtype: string - name: player4 dtype: string - name: rating4 dtype: string - name: player5 dtype: string - name: rating5 dtype: string - name: player6 dtype: string - name: rating6 dtype: string - name: player7 dtype: string - name: rating7 dtype: string - name: player8 dtype: string - name: rating8 dtype: string - name: player9 dtype: string - name: rating9 dtype: string - name: player10 dtype: string - name: rating10 dtype: string - name: player11 dtype: string - name: rating11 dtype: string - name: player12 dtype: string - name: rating12 dtype: string - name: player13 dtype: string - name: rating13 dtype: string - name: player14 dtype: string - name: rating14 dtype: string - name: player15 dtype: string - name: rating15 dtype: string - name: player16 dtype: string - name: rating16 dtype: string - name: player17 dtype: string - name: rating17 dtype: string - name: player18 dtype: string - name: rating18 dtype: string - name: player19 dtype: string - name: rating19 dtype: string - name: player20 dtype: string - name: rating20 dtype: string - name: player21 dtype: string - name: rating21 dtype: string - name: player22 dtype: string - name: rating22 dtype: string - name: player23 dtype: string - name: rating23 dtype: string - name: player24 dtype: string - name: rating24 dtype: string - name: player25 dtype: string - name: rating25 dtype: string - name: player26 dtype: string - name: rating26 dtype: string - name: player27 dtype: string - name: rating27 dtype: string - name: player28 dtype: string - name: rating28 dtype: string - name: player29 dtype: string - name: rating29 dtype: string - name: player30 dtype: string - name: rating30 dtype: string - name: player31 dtype: string - name: rating31 dtype: string - name: player32 dtype: string - name: rating32 dtype: string - name: player33 dtype: string - name: rating33 dtype: string splits: - name: train num_bytes: 13072462 num_examples: 1996 download_size: 6901926 dataset_size: 13072462 --- # Dataset Card for "socce_report_analysis" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jilp00/youtoks-transformers	--- dataset_info: features: - name: text dtype: string - name: token_count dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 2092099 num_examples: 1390 download_size: 1025873 dataset_size: 2092099 configs: - config_name: default data_files: - split: train path: data/train-* ---
Team-PIXEL/PIXELSum_en_wiki_for_TA	--- license: apache-2.0 dataset_info: features: - name: text struct: - name: bytes dtype: binary - name: path dtype: 'null' - name: target dtype: string - name: num_text_patches dtype: int64 splits: - name: train num_bytes: 288179303249 num_examples: 29404255 download_size: 281239419405 dataset_size: 288179303249 configs: - config_name: default data_files: - split: train path: data/train-* ---
polinaeterna/select_true	--- dataset_info: features: - name: '''; select true; --' dtype: int64 splits: - name: train num_bytes: 40 num_examples: 5 download_size: 951 dataset_size: 40 --- # Dataset Card for "select_true" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/quirky_sciq	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: choices sequence: string - name: label dtype: int64 - name: difficulty dtype: float64 - name: statement dtype: string - name: character dtype: string - name: alice_label dtype: bool - name: bob_label dtype: bool splits: - name: train num_bytes: 5973156 num_examples: 9629 - name: validation num_bytes: 1186489 num_examples: 2000 - name: test num_bytes: 1186972 num_examples: 2000 download_size: 1782280 dataset_size: 8346617 --- # Dataset Card for "quirky_sciq" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nicolas-BZRD/Parallel_Global_Voices_English_French	--- license: cc-by-3.0 dataset_info: features: - name: en dtype: string - name: fr dtype: string splits: - name: train num_bytes: 89720129 num_examples: 342060 download_size: 57746668 dataset_size: 89720129 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - translation language: - en - fr tags: - parallel - parallel data size_categories: - 100K<n<1M --- # Parallel Global Voices (English-French) Parallel Global Voices EN-FR is a parallel corpus generated from the Global Voices multilingual group of websites (http://globalvoices.org/), where volunteers publish and translate news stories in more than 40 languages. The original content from the Global Voices websites is available by the authors and publishers under a Creative Commons Attribution license. The content was crawled in July-August 2015 by researchers at the NLP group of the Institute for Language and Speech Processing. Documents that are translations of each other were paired on the basis of their link information. After document pairing, segment alignments were automatically extracted. The results of the automatic alignment at document and segment level are distributed under a Creative Commons Attribution license. ### Attribution details Parallel Global Voices (English - French) was created for the European Language Resources Coordination Action (ELRC) (http://lr-coordination.eu/) by researchers at the NLP group of the Institute for Language and Speech Processing (http://www.ilsp.gr/) with primary data copyrighted by Parallel Global Voices (https://globalvoices.org/) and is licensed under "CC-BY 3.0" (https://creativecommons.org/licenses/by/3.0/).
ramsel/dataviz-sample	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9022 num_examples: 11 download_size: 8204 dataset_size: 9022 --- # Dataset Card for "dataviz-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Deojoandco/fnli	--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 61159046 num_examples: 550152 - name: validation num_bytes: 1120856 num_examples: 10000 - name: test num_bytes: 1117922 num_examples: 10000 download_size: 20299372 dataset_size: 63397824 --- # Dataset Card for "fnli" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/cassin_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of cassin/カッシン/卡辛 (Azur Lane) This is the dataset of cassin/カッシン/卡辛 (Azur Lane), containing 57 images and their tags. The core tags of this character are `long_hair, black_hair, hair_ornament, hairclip, mole_under_eye, mole, low_ponytail, yellow_eyes, breasts, bangs, heterochromia, red_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:-----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 57 \| 43.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 57 \| 33.28 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 124 \| 61.83 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 57 \| 41.56 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 124 \| 74.82 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/cassin_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/cassin_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 \| 18 \| ![](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, looking_at_viewer, solo, bare_shoulders, off_shoulder, collarbone, shirt, blush, black_thighhighs, simple_background, white_background, black_jacket, cleavage, brown_eyes, thigh_strap \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, pleated_skirt, serafuku, short_sleeves, white_shirt, blush, closed_mouth, plaid_skirt, solo, cross_earrings, looking_at_viewer, bike_shorts, blue_sailor_collar, green_bowtie, holding_phone, looking_at_phone, miniskirt, official_alternate_costume, shorts_under_skirt, smartphone \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| bare_shoulders \| off_shoulder \| collarbone \| shirt \| blush \| black_thighhighs \| simple_background \| white_background \| black_jacket \| cleavage \| brown_eyes \| thigh_strap \| pleated_skirt \| serafuku \| short_sleeves \| white_shirt \| closed_mouth \| plaid_skirt \| cross_earrings \| bike_shorts \| blue_sailor_collar \| green_bowtie \| holding_phone \| looking_at_phone \| miniskirt \| official_alternate_costume \| shorts_under_skirt \| smartphone \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:-----------------\|:---------------\|:-------------\|:--------\|:--------\|:-------------------\|:--------------------\|:-------------------\|:---------------\|:-----------\|:-------------\|:--------------\|:----------------\|:-----------\|:----------------\|:--------------\|:---------------\|:--------------\|:-----------------\|:--------------\|:---------------------\|:---------------\|:----------------\|:-------------------\|:------------\|:-----------------------------\|:---------------------\|:-------------\| \| 0 \| 18 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
re2panda/click_bate_1000_final	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: output dtype: string - name: input dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 35358262.8 num_examples: 11400 - name: test num_bytes: 1860961.2 num_examples: 600 download_size: 20825026 dataset_size: 37219224.0 --- # Dataset Card for "click_bate_1000_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdaptLLM/Headline	--- configs: - config_name: Headline data_files: - split: train path: train.csv - split: test path: test.csv task_categories: - text-classification - question-answering - zero-shot-classification language: - en tags: - finance --- # Domain Adaptation of Large Language Models This repo contains the Headline dataset used in our ICLR 2024 paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to transform large-scale pre-training corpora into reading comprehension texts, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗 ************************** Updates ************************** * 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets * 2024/1/16: 🎉 Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!🎉 * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B. * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B. * 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B. ## Domain-Specific LLaMA-1 ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if our method is similarly effective for larger-scale models, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ## Domain-Specific LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our reading comprehension can perfectly fit the data format by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) ## Domain-Specific Tasks ### Pre-templatized/Formatted Testing Splits To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ### Raw Datasets We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: - [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt) - [RCT](https://huggingface.co/datasets/AdaptLLM/RCT) - [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) - [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA) - [Headline](https://huggingface.co/datasets/AdaptLLM/Headline) - [NER](https://huggingface.co/datasets/AdaptLLM/NER) - [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code: ```python from datasets import load_dataset # MQP: dataset = load_dataset('medical_questions_pairs') # PubmedQA: dataset = load_dataset('bigbio/pubmed_qa') # USMLE: dataset=load_dataset('GBaker/MedQA-USMLE-4-options') # SCOTUS dataset = load_dataset("lex_glue", 'scotus') # CaseHOLD dataset = load_dataset("lex_glue", 'case_hold') # UNFAIR-ToS dataset = load_dataset("lex_glue", 'unfair_tos') ``` ## Citation If you find our work helpful, please cite us: ```bibtex @inproceedings{ cheng2024adapting, title={Adapting Large Language Models via Reading Comprehension}, author={Daixuan Cheng and Shaohan Huang and Furu Wei}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=y886UXPEZ0} } ``` and the original dataset: ```bibtex @article{Headline, author = {Ankur Sinha and Tanmay Khandait}, title = {Impact of News on the Commodity Market: Dataset and Results}, journal = {CoRR}, volume = {abs/2009.04202}, year = {2020} } ```
rushdiodeh/rush	--- license: apache-2.0 task_categories: - token-classification language: - ar tags: - not-for-all-audiences size_categories: - 10B<n<100B ---
MCG-NJU/MultiSports	--- annotations_creators: - crowdsourced language: - en language_creators: - expert-generated license: - cc-by-nc-4.0 multilinguality: - monolingual pretty_name: MultiSports size_categories: [] source_datasets: - original tags: - video - action detection - spatial-temporal action localization task_categories: - image-classification - object-detection - other task_ids: - multi-class-image-classification extra_gated_heading: "Acknowledge license to accept the repository" extra_gated_prompt: "This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License" extra_gated_fields: I agree to use this dataset for non-commerical use ONLY: checkbox --- # Dataset Card for MultiSports ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://deeperaction.github.io/datasets/multisports.html - Repository: https://github.com/MCG-NJU/MultiSports - Paper: https://arxiv.org/abs/2105.07404 - Leaderboard: https://paperswithcode.com/dataset/multisports - Point of Contact: mailto: runyu_he@smail.nju.edu.cn ### Dataset Summary Spatio-temporal action localization is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. MultiSports is a multi-person dataset of spatio-temporal localized sports actions. Please refer to [this paper](https://arxiv.org/abs/2105.07404) for more details. Please refer to [this repository](https://github.com/MCG-NJU/MultiSports) for evaluation. ### Supported Tasks and Leaderboards - `Spatial-temporal action localization` Details about evaluation can be found in the [GitHub Repository](https://github.com/mcG-NJU/MultiSports). Previous challenge results can be found in [this page](https://deeperaction.github.io/results/index.html) and [this CodaLab challenge](https://codalab.lisn.upsaclay.fr/competitions/3736). ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances Demo is available on [dataset homepage](https://deeperaction.github.io/datasets/multisports.html). The dataset contains ```rawframes.tar``` and ```multisports_GT.pkl```. The GT pkl file is a dictionary with the following structure: ``` { 'labels': ['label1', 'label2', ...], 'train_videos': [['train_vid_1', 'train_vid_2', ...]], 'test_videos': [['test_vid_1', 'test_vid_2', ...]], 'nframes': { 'vid_1': nframes_1, 'vid_2': nframes_2, ... }, 'resolution': { 'vid_1': resolution_1, 'vid_2': resolution_2, ... }, 'gttubes': { 'vid_1': { 'label_1': [tube_1, tube_2, ...], 'label_2': [tube_1, tube_2, ...], ... } ... } } ``` Here a ```tube``` is a ```numpy.ndarray``` with ```nframes``` rows and 5 columns ```<frame number> <x1> <y1> <x2> <y2>```. ### Data Fields Raw frames are organized according to their sport category. The pickle file of GT contains the following fields. - labels: list of labels - train_videos: a list with one split element containing the list of training videos - test_videos: a list with one split element containing the list of validation videos - nframes: dictionary that gives the number of frames for each video - resolution: dictionary that output a tuple ```(h,w)``` of the resolution for each video - gttubes: dictionary that contains the gt tubes for each video. Gt tubes are dictionary that associates from each index of label, a list of tubes. A ```tube``` is a ```numpy.ndarray``` with ```nframes``` rows and 5 columns ```<frame number> <x1> <y1> <x2> <y2>```. Please note that the label index starts from 0 and the frame index starts from 1. For the label index ```i```, the label name is ```labels[i]```. <details> <summary> Click here to see the full list of MultiSports class labels mapping: </summary> \|id\|Class\| \|--\|-----\| \| 0 \| aerobic push up \| \| 1 \| aerobic explosive push up \| \| 2 \| aerobic explosive support \| \| 3 \| aerobic leg circle \| \| 4 \| aerobic helicopter \| \| 5 \| aerobic support \| \| 6 \| aerobic v support \| \| 7 \| aerobic horizontal support \| \| 8 \| aerobic straight jump \| \| 9 \| aerobic illusion \| \| 10 \| aerobic bent leg(s) jump \| \| 11 \| aerobic pike jump \| \| 12 \| aerobic straddle jump \| \| 13 \| aerobic split jump \| \| 14 \| aerobic scissors leap \| \| 15 \| aerobic kick jump \| \| 16 \| aerobic off axis jump \| \| 17 \| aerobic butterfly jump \| \| 18 \| aerobic split \| \| 19 \| aerobic turn \| \| 20 \| aerobic balance turn \| \| 21 \| volleyball serve \| \| 22 \| volleyball block \| \| 23 \| volleyball first pass \| \| 24 \| volleyball defend \| \| 25 \| volleyball protect \| \| 26 \| volleyball second pass \| \| 27 \| volleyball adjust \| \| 28 \| volleyball save \| \| 29 \| volleyball second attack \| \| 30 \| volleyball spike \| \| 31 \| volleyball dink \| \| 32 \| volleyball no offensive attack \| \| 33 \| football shoot \| \| 34 \| football long pass \| \| 35 \| football short pass \| \| 36 \| football through pass \| \| 37 \| football cross \| \| 38 \| football dribble \| \| 39 \| football trap \| \| 40 \| football throw \| \| 41 \| football diving \| \| 42 \| football tackle \| \| 43 \| football steal \| \| 44 \| football clearance \| \| 45 \| football block \| \| 46 \| football press \| \| 47 \| football aerial duels \| \| 48 \| basketball pass \| \| 49 \| basketball drive \| \| 50 \| basketball dribble \| \| 51 \| basketball 3-point shot \| \| 52 \| basketball 2-point shot \| \| 53 \| basketball free throw \| \| 54 \| basketball block \| \| 55 \| basketball offensive rebound \| \| 56 \| basketball defensive rebound \| \| 57 \| basketball pass steal \| \| 58 \| basketball dribble steal \| \| 59 \| basketball interfere shot \| \| 60 \| basketball pick-and-roll defensive \| \| 61 \| basketball sag \| \| 62 \| basketball screen \| \| 63 \| basketball pass-inbound \| \| 64 \| basketball save \| \| 65 \| basketball jump ball \| </details> ### Data Splits \| \|train \|validation\| test \| \|-------------\|------:\|---------:\|------:\| \|# of tubes \|28514 \|10116 \| - \| GT for test split is not provided. Please wait for the new competition to start. Information will be updated in [dataset homepage](https://deeperaction.github.io/datasets/multisports.html). ## Dataset Creation ### Curation Rationale Spatio-temporal action detection is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. ### Source Data #### Initial Data Collection and Normalization > After choosing the four sports, we search for their competition videos by querying the name of sports like volleyball and the name of competition levels like Olympics and World Cup on YouTube, and then down- load videos from top search results. For each video, we only select high-resolution, e.g. 720P or 1080P, competition records and then manually cut them into clips of minutes, with less shot changes in each clip and to be more suitable for action detection. #### Who are the source language producers? The annotators of action categories and temporal boundaries are professional athletes of the corresponding sports. Please refer to [the paper](https://arxiv.org/abs/2105.07404) for more information. ### Annotations #### Annotation process 1. (FIRST STAGE) A team of professional athletes generate records of the action la- bel, the starting and ending frame, and the person box in the starting frame, which can ensure the efficiency, accu- racy and consistency of our annotation results. 2. At least one annotator with domain knowledge double-check the annotations, correct wrong or inaccurate ones and also add missing annotations 3. (SECOND STAGE) With the help of FCOT tracking algorithm, a team of crowd-sourced annotators adjust bounding boxes of tracking results at each frame for each record. 4. Double-check each instance by playing it in 5fps and manually correct the inaccurate bounding boxes. #### Who are the annotators? For the first stage, annotators are professional athletes. For the second stage, annotators are common volunteers. ### 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 Authors of [this paper](https://arxiv.org/abs/2105.07404) - Yixuan Li - Lei Chen - Runyu He - Zhenzhi Wang - Gangshan Wu - Limin Wang ### Licensing Information <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>. ### Citation Information If you find this dataset useful, please cite as ``` @InProceedings{Li_2021_ICCV, author = {Li, Yixuan and Chen, Lei and He, Runyu and Wang, Zhenzhi and Wu, Gangshan and Wang, Limin}, title = {MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {13536-13545} } ``` ### Contributions Thanks to [@Judie1999](https://github.com/Judie1999) for adding this dataset.
WangResearchLab/AgentInstruct	--- configs: - config_name: default data_files: - split: agentinstruct_instruction path: instructions.parquet language: - en size_categories: - n<1K --- # AgentInstruct: Agent Instructs Large Language Models to be General Zero-Shot Reasoners The repo for paper [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://arxiv.org/abs/2310.03710). <p align="center"> 📃 <a href="https://arxiv.org/abs/2310.03710" target="_blank">[Paper]</a> • 💻 <a href="https://github.com/wang-research-lab/agentinstruct" target="_blank">[Github]</a> • 🤗 <a href="https://huggingface.co/datasets/WangResearchLab/AgentInstruct" target="_blank">[HuggingFace]</a> • 📌 <a href="https://nlp.wustl.edu/blog/2023-11-02-agentinstruct/" target="_blank">[Blog]</a> • 📽 <a href="http://cgraywang.github.io/files/2023-agentinstruct-slides(10min).pdf" target="_blank">[Slides]</a> • 📋 <a href="http://cgraywang.github.io/files/2023-agentinstruct-poster.pdf" target="_blank">[Poster]</a> </p> ## AgentInstruct Instruction Dataset The AgentInstruct Instruction dataset contains agent instructions for the 29 datasets used in the paper. We encourage you to use our AgentInstruct methodology detailed in the paper and code to produce more instructions and evaluate on more datasets. We provide an example of using the instructions and producing more instructions with our AgentInstruct below. The AgentInstruct Instruction dataset we used in the code is [here](https://huggingface.co/datasets/WangResearchLab/AgentInstruct/blob/main/instructions.json). ## Installation Begin by cloning this repository: ``` git clone --recurse-submodules https://github.com/wang-research-lab/agentinstruct.git ``` Then, run the following to implement zero-shot AgentInstruct into the HELM submodule: ``` cd agentinstruct bash src/agentinstruct/reasoning/helm_updates/update_helm.sh ``` Now, add the following api keys to `prod_env/credentials.conf`: `openaiApiKey` (from [here](https://openai.com/blog/openai-api)) and `bingSubscriptionKey` (from [here](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api)). Use the following format: ``` openaiApiKey: [your key here] bingSubscriptionKey: [your key here] ``` We would recommend using a [Python 3.10 docker image](https://hub.docker.com/layers/library/python/3.10/images/sha256-6eff601177b9fdfb85f383089b97468910ff59be129019b1588dc3f9ac862204?context=explore). ``` docker network create mynetwork docker pull python:3.10 docker run --network=mynetwork -v ~/agentinstruct:/code/agentinstruct -it python:3.10 bash ``` Next, create a virtual enviroment: ``` cd /code/agentinstruct python3 -m pip install virtualenv python3 -m virtualenv -p python3.10 helm-venv source helm-venv/bin/activate ``` Run the following to download the necessary dependencies: ``` pip install -e src/agentinstruct/reasoning/helm pip install -r requirements.txt ``` Note: For running other models (vicuna-13b, llama-2-7b-chat, llama-2-13b-chat, llama-2-70b-chat), you must also follow the instructions [here](src/agentinstruct/reasoning/serve/README.md). ## Replicating Main Results To replicate the main results on 28 datasets (excludes NewsQA for its license restrictions, see [here](src/agentinstruct/reasoning/helm_updates/src/helm/benchmark/scenarios/newsqa_scenario.py)) with a specific model (gpt-3.5-turbo, llama-2-7b-chat, llama-2-13b-chat, llama-2-70b-chat, vicuna-13b), run: ``` bash scripts/gpt-3.5-turbo.sh bash scripts/llama-2-7b-chat.sh bash scripts/llama-2-13b-chat.sh bash scripts/llama-2-70b-chat.sh bash scripts/vicuna-13b.sh ``` Results will be stored in ```benchmark_outputs/runs/{model}-agentinstruct/results.csv```. ## Customizing your Run There are three key components of the zero-shot AgentInstruct pipeline: (1) generating agent instructions, (2) running reasoning steps with the instructions, and (3) formatting the results. In this section, we will look at each component in detail, focusing on a single dataset: AddSub. Note that nothing here is specific to AddSub, and can be applied to any dataset, or even a combination of datasets! ### Generating Agent Instructions First, to generate the agent instructions for AddSub, run the following: ``` bash scripts/generate_agent_instructions.sh scripts/run_specs/simple-gpt-3.5-turbo.conf addsub ``` We'll create a configuration file that specifies the run configuration. As an example, we'll look at the configuration file ```scripts/run_specs/simple-gpt-3.5-turbo.conf```, which specifies the configuration of running the AddSub dataset using GPT-3.5 Turbo: ``` entries: [ {description: "addsub:model=openai/gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct", priority: 1} ] ``` The agent instructions for the AddSub dataset will be saved in ```instructions/addsub/instructions.json```. The agent's input, as well as the web sources used and intermediate prompts, will be saved under ```instructions/addsub/inputs.json``` and ```instructions/addsub/metadata.json``` respectively. ### Running Reasoning Steps We'll use the same configuration file as above. To run reasoning steps with zero-shot AgentInstruct on AddSub, run the following: ``` bash scripts/run_reasoning.sh scripts/run_specs/simple-gpt-3.5-turbo.conf addsub 1000 ``` The first two parameters are identical to those above, and the third represents the number of instances to run reasoning steps on. The results will be stored in ```benchmark_outputs/runs/addsub```. Note: By default, zero-shot AgentInstruct reasoning will be done using the latest set of instructions generated. To run reasoning with the instructions used in the paper, run this script before the run_reasoning command: ``` python scripts/replicate.py ``` ### Format Results To easily format the evaluation results, run: ``` python src/agentinstruct/eval/format_results.py --suite addsub ``` The evaluation results will be saved in ```benchmark_output/runs/addsub/results.csv```. To see the full text output by instance, open ```benchmark_output/runs/addsub/'addsub:model=openai_gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct'/scenario_state.json``` and search for ```full_text```. Note: Normally, the results are formatted after all the run spec descriptions in the configuration file have been run. To see for a single run spec description, view: ``` benchmark_output/runs/addsub/'addsub:model=openai_gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct'/stats.json ``` ### All Together Now To run the above entire AgentInstruct pipeline in one go, run: ``` bash scripts/run.sh scripts/run_specs/simple-gpt-3.5-turbo.conf addsub 1000 ``` This will run all 3 steps outlined above, and store the result in ```benchmark_outputs/runs/addsub```. ## Arguments In this section, we'll cover various important run arguments. ### Run Configuration Arguments A run spec describes a specific dataset to run. For example, the run spec for AddSub used above is: ``` {description: "addsub:model=openai/gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct", priority: 1} ``` \| argument \| description \| options\| \|----\|----\|----\| \| `model` \| Model to use for inference. \| `local/vicuna-13b` <br> `local/llama-2-7b-chat` <br> `local/llama-2-13b-chat` <br> `local/llama-2-70b-chat` <br> `openai/gpt-3.5-turbo-0301` \| \| `max_train_instances` \| Number of few shot examples to prepend. Few Shot is not recommended. \| int \| \| `instructions` \| Optional prompting method to use. `None` corresponds to standard zeroshot. \| `agentinstruct` <br> `zeroshotcot` <br> `None` \| Note: Several datasets have additional argument to specify the specific subset or task. ### Generating Agent Instructions Arguments The main script to generate agent instructions is ```scripts/generate_agent_instructions.sh```. It takes the following 2 positional arguments: \| argument \| description \| options\| \|----\|----\|----\| \| 1st \| Path to run spec file. \| str \| \| 2nd \| Suite name under which to save instructions. \| str \| Internally, the agent instructions are generated by first running dataset preprocessing (in ```src/agentinstruct/agent/utils/dataset_preprocessing.py```) and then running the instruction generation (in ```src/agentinstruct/agent/agent_instr_generation.py```). These are combined in ```src/agentinstruct/agent/agent_pipeline.py``` and called by ```scripts/generate_agent_instructions.sh```. GPT-4 is used as the agent LLM as in our paper. ### Running Reasoning Arguments The main script to run reasoning is ```scripts/run_reasoning.sh```, which internally calls `helm-run`. It takes the following 4 positional arguments, as well as a placeholder for any additional argument to pass to `helm-run`: \| argument \| description \| options\| \|----\|--------------------------------------------------------------------------------------\|----\| \| 1st \| Path to run spec file. \| str \| \| 2nd \| Suite name under which to save outputs. \| str \| \| 3rd \| Maximum number of instances to run. \| int \| \| 4th \| Maximum number of threads from which to send requests. Defaults to 8 for all models. \| int \| \| 5th \| Place holder for any additional argument to pass to `helm-run`. \| str \| ### Outputting Results Arguments The main script to format the results is ```src/agentinstruct/eval/format_results.py```. It takes a single named argument: \| argument \| description \| options\| \|----\|----\|----\| \| --suite \| Suite name under which to find outputs. \| str \| ## Replicating Additional Results To replicate the zero-shot (`zeroshot`) and zero-shot CoT (`zeroshot`) modes, run: ``` bash scripts/run_reasoning.sh scripts/run_specs/{mode}/{model}-{mode}.conf {model}-{mode} 1000 8 python src/agentinstruct/eval/format_results.py --suite {model}-{mode} ``` where `{mode}` is `zeroshot` or `zeroshotcot` and `{model}` is `vicuna-13b`, `llama-2-7b-chat`, `llama-2-13b-chat`, `llama-2-70b-chat`, or `gpt-3.5-turbo`. Note: For standard zero-shot runs, pass `skip-expander` as the 5th positional argument. ## Citation ```bibtex @article{crispino2023agent, title={Agent Instructs Large Language Models to be General Zero-Shot Reasoners}, author={Crispino, Nicholas and Montgomery, Kyle and Zeng, Fankun and Song, Dawn and Wang, Chenguang}, journal={arXiv preprint arXiv:2310.03710}, year={2023} } ```
atmallen/quirky_sciq_pythia-410m_alice	--- dataset_info: features: - name: id dtype: string - name: choices sequence: string - name: label dtype: int64 - name: difficulty dtype: float64 - name: statement dtype: string - name: character dtype: string - name: alice_label dtype: bool - name: bob_label dtype: bool - name: bob_log_odds dtype: float64 splits: - name: train num_bytes: 14551988.0 num_examples: 23358 - name: validation num_bytes: 1232235.0 num_examples: 2000 - name: test num_bytes: 1255333.0 num_examples: 2000 download_size: 5454709 dataset_size: 17039556.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
ibranze/araproje_hellaswag_tr_f1	--- dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string splits: - name: validation num_bytes: 162703.0 num_examples: 250 download_size: 88680 dataset_size: 162703.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_hellaswag_tr_f1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B-v2	--- pretty_name: Evaluation run of openaccess-ai-collective/DPOpenHermes-7B-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [openaccess-ai-collective/DPOpenHermes-7B-v2](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2)\ \ 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_openaccess-ai-collective__DPOpenHermes-7B-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T15:48:02.975332](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B-v2/blob/main/results_2023-12-09T15-48-02.975332.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\": 0.6393858131029347,\n\ \ \"acc_stderr\": 0.03231519248140217,\n \"acc_norm\": 0.6405744963876552,\n\ \ \"acc_norm_stderr\": 0.032967768680137746,\n \"mc1\": 0.412484700122399,\n\ \ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5922184046952629,\n\ \ \"mc2_stderr\": 0.015444038493597899\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6348122866894198,\n \"acc_stderr\": 0.014070265519268802,\n\ \ \"acc_norm\": 0.6663822525597269,\n \"acc_norm_stderr\": 0.013778687054176536\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.664708225453097,\n\ \ \"acc_stderr\": 0.004711275408138421,\n \"acc_norm\": 0.8522206731726748,\n\ \ \"acc_norm_stderr\": 0.0035415582637791008\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\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\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438665,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438665\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\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\"\ : 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.036928207672648664\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\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\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\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305526,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305526\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.02425107126220884,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.02425107126220884\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790492,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790492\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396997,\n\ \ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396997\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887048,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887048\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\"\ : 0.8275229357798165,\n \"acc_stderr\": 0.01619780795684804,\n \"\ acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.01619780795684804\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\"\ : 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\ acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621115,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621115\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094632,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094632\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8058252427184466,\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\": 0.022509033937077805,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077805\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.013890862162876166,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.013890862162876166\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3642458100558659,\n\ \ \"acc_stderr\": 0.016094338768474596,\n \"acc_norm\": 0.3642458100558659,\n\ \ \"acc_norm_stderr\": 0.016094338768474596\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.02505850331695814,\n\ \ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.02505850331695814\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.026003301117885142,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.026003301117885142\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\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\": 0.46740547588005216,\n\ \ \"acc_stderr\": 0.012743072942653345,\n \"acc_norm\": 0.46740547588005216,\n\ \ \"acc_norm_stderr\": 0.012743072942653345\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.027678468642144724,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.027678468642144724\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6879084967320261,\n \"acc_stderr\": 0.018745011201277657,\n \ \ \"acc_norm\": 0.6879084967320261,\n \"acc_norm_stderr\": 0.018745011201277657\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.412484700122399,\n\ \ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5922184046952629,\n\ \ \"mc2_stderr\": 0.015444038493597899\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7916337805840569,\n \"acc_stderr\": 0.011414554399987727\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6360879454131918,\n \ \ \"acc_stderr\": 0.013252539227966195\n }\n}\n```" repo_url: https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|arc:challenge\|25_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-09T15-48-02.975332.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|gsm8k\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hellaswag\|10_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T15-48-02.975332.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T15-48-02.975332.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '/details_harness\|winogrande\|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-09T15-48-02.975332.parquet' - config_name: results data_files: - split: 2023_12_09T15_48_02.975332 path: - results_2023-12-09T15-48-02.975332.parquet - split: latest path: - results_2023-12-09T15-48-02.975332.parquet --- # Dataset Card for Evaluation run of openaccess-ai-collective/DPOpenHermes-7B-v2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [openaccess-ai-collective/DPOpenHermes-7B-v2](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2) 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_openaccess-ai-collective__DPOpenHermes-7B-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T15:48:02.975332](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B-v2/blob/main/results_2023-12-09T15-48-02.975332.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": 0.6393858131029347, "acc_stderr": 0.03231519248140217, "acc_norm": 0.6405744963876552, "acc_norm_stderr": 0.032967768680137746, "mc1": 0.412484700122399, "mc1_stderr": 0.01723329939957122, "mc2": 0.5922184046952629, "mc2_stderr": 0.015444038493597899 }, "harness\|arc:challenge\|25": { "acc": 0.6348122866894198, "acc_stderr": 0.014070265519268802, "acc_norm": 0.6663822525597269, "acc_norm_stderr": 0.013778687054176536 }, "harness\|hellaswag\|10": { "acc": 0.664708225453097, "acc_stderr": 0.004711275408138421, "acc_norm": 0.8522206731726748, "acc_norm_stderr": 0.0035415582637791008 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "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": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438665, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438665 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "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": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "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": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "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": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305526, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305526 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7612903225806451, "acc_stderr": 0.02425107126220884, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.02425107126220884 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8, "acc_stderr": 0.031234752377721175, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721175 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.029620227874790492, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.029620227874790492 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396997, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396997 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253255, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887048, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887048 }, "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": 0.8275229357798165, "acc_stderr": 0.01619780795684804, "acc_norm": 0.8275229357798165, "acc_norm_stderr": 0.01619780795684804 }, "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": 0.7990196078431373, "acc_stderr": 0.028125972265654373, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.028125972265654373 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621115, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621115 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094632, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094632 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077805, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077805 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.013890862162876166, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.013890862162876166 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.02425790170532338, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.02425790170532338 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3642458100558659, "acc_stderr": 0.016094338768474596, "acc_norm": 0.3642458100558659, "acc_norm_stderr": 0.016094338768474596 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7418300653594772, "acc_stderr": 0.02505850331695814, "acc_norm": 0.7418300653594772, "acc_norm_stderr": 0.02505850331695814 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.026003301117885142, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.026003301117885142 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "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": 0.46740547588005216, "acc_stderr": 0.012743072942653345, "acc_norm": 0.46740547588005216, "acc_norm_stderr": 0.012743072942653345 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7058823529411765, "acc_stderr": 0.027678468642144724, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.027678468642144724 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6879084967320261, "acc_stderr": 0.018745011201277657, "acc_norm": 0.6879084967320261, "acc_norm_stderr": 0.018745011201277657 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784593, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784593 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786845, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786845 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "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": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.412484700122399, "mc1_stderr": 0.01723329939957122, "mc2": 0.5922184046952629, "mc2_stderr": 0.015444038493597899 }, "harness\|winogrande\|5": { "acc": 0.7916337805840569, "acc_stderr": 0.011414554399987727 }, "harness\|gsm8k\|5": { "acc": 0.6360879454131918, "acc_stderr": 0.013252539227966195 } } ``` ### 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]
HoangLe1312/codecontest-editorials	--- dataset_info: features: - name: contest dtype: string - name: note dtype: string - name: editorial dtype: string - name: problems list: - name: content dtype: string - name: index dtype: string - name: note dtype: string splits: - name: train num_bytes: 15464367 num_examples: 872 download_size: 7414336 dataset_size: 15464367 configs: - config_name: default data_files: - split: train path: data/train-* ---
bsgreenb/cats_vs_dogs	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': cat '1': dog - name: id dtype: int32 splits: - name: train num_bytes: 565589330.0 num_examples: 25000 - name: test num_bytes: 286421182.5 num_examples: 12500 download_size: 859839390 dataset_size: 852010512.5 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
hieunguyen1053/htpl	--- dataset_info: features: - name: init_url dtype: string - name: url dtype: string - name: html dtype: string - name: question dtype: string - name: answer dtype: string - name: new_question dtype: string - name: new_answer dtype: string splits: - name: train num_bytes: 1844692069 num_examples: 22528 download_size: 690698519 dataset_size: 1844692069 configs: - config_name: default data_files: - split: train path: data/train-* ---
turkmen/dipperTR	--- license: openrail language: - tr ---
fraviofranco/vozcortella	--- license: openrail ---
misshimichka/flower_faces_dataset_v2	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 239848099.0 num_examples: 114 download_size: 239859641 dataset_size: 239848099.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-100000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 983737 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
andyP/fake_news_en_opensources	--- license: apache-2.0 annotations_creators: - expert-generated language_creators: - found task_categories: - text-classification language: - en multilinguality: - monolingual source_datasets: - Opensources https://github.com/BigMcLargeHuge/opensources - FakeNews Corpus https://github.com/several27/FakeNewsCorpus tags: - fake-news-detection - fake news - english - nlp task_ids: - topic-classification - fact-checking pretty_name: Fake News Opensources size_categories: - 1M<n<10M dataset_info: features: - name: id dtype: int64 - name: type dtype: string - name: domain dtype: string - name: scraped_at dtype: string - name: url dtype: string - name: authors dtype: string - name: title dtype: string - name: content dtype: string --- # Dataset Card for "Fake News Opensources" ## 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 <!-- - Paper: Fake News Opensources --> - Homepage: [https://github.com/AndyTheFactory/FakeNewsDataset](https://github.com/AndyTheFactory/FakeNewsDataset) - Repository: [https://github.com/AndyTheFactory/FakeNewsDataset](https://github.com/AndyTheFactory/FakeNewsDataset) - Point of Contact: [Andrei Paraschiv](https://github.com/AndyTheFactory) - ### Dataset Summary a consolidated and cleaned up version of the opensources Fake News dataset Fake News Corpus comprises 8,529,090 individual articles, classified into 12 classes: reliable, unreliable, political, bias, fake, conspiracy, rumor clickbait, junk science, satire, hate and unknown. The articles were scraped between the end of 2017 and the beginning of 2018 from various news websites, totaling 647 distinct sources, collecting articles dating from various years leading to the 2016 US elections and the year after. Documents were classified based on their source, based on the curated website list provided by opensources.co using a leading to a high imbalanced class distribution. Their proposed source classification method, was based on six criteria: - Title and Domain name analysis, - “About Us” analysis, - source or study mentioning, - writing style analysis, - aesthetic analysis and social media analysis. After extensive data cleaning and duplicate removal we retain 5,915,569 records ### Languages English ## Dataset Structure ### Data Instances An example record looks as follows. ``` { 'id': 4059480, 'type': 'political', 'domain': 'dailycaller.com', 'scraped_at': '2017-11-27', 'url': 'http://dailycaller.com/buzz/massachusettsunited-states/page/2/', 'authors': 'Jeff Winkler, Jonathan Strong, Ken Blackwell, Pat Mcmahon, Julia Mcclatchy, Admin, Matt Purple', 'title': 'The Daily Caller', 'content':'New Hampshire is the state with the highest median income in the nation, according to the U.S. Census Bureauâ€™s report on income, poverty and health insurance', } ``` ### Data Fields - `id`: The unique article ID - `type`: the label of the record (one of: reliable, unreliable, political, bias, fake, conspiracy, rumor clickbait, junk science, satire, hate) - 'scraped_at': date of the original scrape run - 'url': original article url - 'authors': comma separated list of scraped authors - 'title': original scraped article title - `content`: full article text ### Data Splits Label \| Nr Records :---\| :---: reliable \| 1807323 political \| 968205 bias \| 769874 fake \| 762178 conspiracy \| 494184 rumor \| 375963 unknown \| 230532 clickbait \| 174176 unreliable \| 104537 satire \| 84735 junksci \| 79099 hate \| 64763 \| total \| 5915569 ## Dataset Creation ### Source Data News Articles from various sites #### Who are the source language producers? News Articles, Blogs ### Annotations #### Who are the annotators? Journalists ### Other Known Limitations The dataset was not manually filtered, therefore some of the labels might not be correct and some of the URLs might not point to the actual articles but other pages on the website. However, because the corpus is intended for use in training machine learning algorithms, those problems should not pose a practical issue. Additionally, when the dataset will be finalised (as for now only about 80% was cleaned and published), I do not intend to update it, therefore it might quickly become outdated for other purposes than content-based algorithms. However, any contributions are welcome! ### Licensing Information This data is available and distributed under Apache-2.0 license ### Citation Information ``` tbd ```
dvgodoy/auto-mpg	--- dataset_info: features: - name: mpg dtype: float64 - name: cylinders dtype: int64 - name: displacement dtype: float64 - name: horsepower dtype: float64 - name: weight dtype: int64 - name: acceleration dtype: float64 - name: model year dtype: int64 - name: origin dtype: int64 - name: car name dtype: string splits: - name: train num_bytes: 33470 num_examples: 398 download_size: 13036 dataset_size: 33470 --- # Dataset Card for "auto-mpg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
roa7n/patched_test_f_UCH_ps_50__v2023d	--- dataset_info: features: - name: id dtype: string - name: sequence_str dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 48828729 num_examples: 110542 download_size: 4291765 dataset_size: 48828729 --- # Dataset Card for "patched_test_f_UCH_ps_50__v2023d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
luisroque/instruct-python-llama2-20k	--- 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: 34661192.7 num_examples: 19000 - name: test num_bytes: 1824273.3 num_examples: 1000 download_size: 19060329 dataset_size: 36485466 license: cc-by-sa-3.0 task_categories: - text-generation language: - en pretty_name: Instruct Python 500k size_categories: - 10K<n<100K --- # Fine-tuning Instruct Llama2 Stack Overflow Python Q&A ## Transformed Dataset ### Objective The transformed dataset is designed for fine-tuning LLMs to improve Python coding assistance by focusing on high-quality content from Stack Overflow. It has around 20k instructions. ### Structure - Question-Answer Pairing: Questions and answers are paired using the `ParentId` linkage. - Quality Focus: Only top-rated answers for each question are retained. - HTML Tag Removal: All HTML tags in the content are removed. - Combined Question Field: Each question's title and body are merged. - Filtering: Entries with negative scores or those not containing Python code structures are excluded. Final columns: - `score_question` - `score_answer` - `question` - `answer` ### Llama2 Transformation The dataset has been transformed to match the Llama2 prompt structure, which is relevant for the model's fine-tuning. The format is the following: `<s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_message }} [/INST]` Where: - `system_prompt` gives context or instructions to the model. - `user_message` is the user's query following the system prompt, expecting a particular response from the model. This structure ensures the training aligns with Llama2's expectations, optimizing the fine-tuning quality. ## Original Dataset The dataset contains questions and answers from Stack Overflow with the `python` tag, covering the period from August 2, 2008, to October 19, 2016. ## License All contributions are under the [CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). Attribution is required. The original dataset was posted [here](https://www.kaggle.com/datasets/stackoverflow/pythonquestions). Keep in touch: [LinkedIn](https://www.linkedin.com/in/luisbrasroque/)
open-llm-leaderboard/details_namirocks__mistral-shishya-all-hal-model-7b-ep3	--- pretty_name: Evaluation run of namirocks/mistral-shishya-all-hal-model-7b-ep3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [namirocks/mistral-shishya-all-hal-model-7b-ep3](https://huggingface.co/namirocks/mistral-shishya-all-hal-model-7b-ep3)\ \ 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_namirocks__mistral-shishya-all-hal-model-7b-ep3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T06:47:44.363242](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__mistral-shishya-all-hal-model-7b-ep3/blob/main/results_2024-01-27T06-47-44.363242.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\": 0.27600273267650555,\n\ \ \"acc_stderr\": 0.031033345939924385,\n \"acc_norm\": 0.27623146997547765,\n\ \ \"acc_norm_stderr\": 0.03186902642010444,\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.3642557797582405,\n\ \ \"mc2_stderr\": 0.014026846292362593\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.3506825938566553,\n \"acc_stderr\": 0.013944635930726087,\n\ \ \"acc_norm\": 0.3796928327645051,\n \"acc_norm_stderr\": 0.014182119866974872\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6009759012148974,\n\ \ \"acc_stderr\": 0.004886969266944266,\n \"acc_norm\": 0.777733519219279,\n\ \ \"acc_norm_stderr\": 0.004149195626910384\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.3851851851851852,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.3851851851851852,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.2641509433962264,\n \"acc_stderr\": 0.02713429162874171,\n\ \ \"acc_norm\": 0.2641509433962264,\n \"acc_norm_stderr\": 0.02713429162874171\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.20833333333333334,\n\ \ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.20833333333333334,\n\ \ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.21965317919075145,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.2765957446808511,\n \"acc_stderr\": 0.029241883869628827,\n\ \ \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.029241883869628827\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\ acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.21428571428571427,\n\ \ \"acc_stderr\": 0.03670066451047182,\n \"acc_norm\": 0.21428571428571427,\n\ \ \"acc_norm_stderr\": 0.03670066451047182\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.24193548387096775,\n \"acc_stderr\": 0.024362599693031086,\n \"\ acc_norm\": 0.24193548387096775,\n \"acc_norm_stderr\": 0.024362599693031086\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"\ acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\"\ : 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.3090909090909091,\n \"acc_stderr\": 0.03608541011573967,\n\ \ \"acc_norm\": 0.3090909090909091,\n \"acc_norm_stderr\": 0.03608541011573967\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.02962022787479049,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.02962022787479049\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.032210245080411544,\n\ \ \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.032210245080411544\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2717948717948718,\n \"acc_stderr\": 0.022556551010132354,\n\ \ \"acc_norm\": 0.2717948717948718,\n \"acc_norm_stderr\": 0.022556551010132354\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275794,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275794\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.029344572500634332,\n\ \ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.029344572500634332\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2052980132450331,\n \"acc_stderr\": 0.03297986648473835,\n \"\ acc_norm\": 0.2052980132450331,\n \"acc_norm_stderr\": 0.03297986648473835\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.27155963302752295,\n \"acc_stderr\": 0.019069098363191445,\n \"\ acc_norm\": 0.27155963302752295,\n \"acc_norm_stderr\": 0.019069098363191445\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.16666666666666666,\n \"acc_stderr\": 0.025416428388767478,\n \"\ acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.025416428388767478\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.5294117647058824,\n \"acc_stderr\": 0.03503235296367994,\n \"\ acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03503235296367994\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.3206751054852321,\n \"acc_stderr\": 0.030381931949990403,\n \ \ \"acc_norm\": 0.3206751054852321,\n \"acc_norm_stderr\": 0.030381931949990403\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.36771300448430494,\n\ \ \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.36771300448430494,\n\ \ \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.24793388429752067,\n \"acc_stderr\": 0.03941897526516303,\n \"\ acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.03941897526516303\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.03487825168497892,\n\ \ \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.03487825168497892\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.26785714285714285,\n\ \ \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n\ \ \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822586,\n\ \ \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822586\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.32051282051282054,\n\ \ \"acc_stderr\": 0.030572811310299607,\n \"acc_norm\": 0.32051282051282054,\n\ \ \"acc_norm_stderr\": 0.030572811310299607\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.3243933588761175,\n\ \ \"acc_stderr\": 0.01674092904716271,\n \"acc_norm\": 0.3243933588761175,\n\ \ \"acc_norm_stderr\": 0.01674092904716271\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23687150837988827,\n\ \ \"acc_stderr\": 0.01421957078810399,\n \"acc_norm\": 0.23687150837988827,\n\ \ \"acc_norm_stderr\": 0.01421957078810399\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875202,\n\ \ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875202\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.1864951768488746,\n\ \ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.24822695035460993,\n \"acc_stderr\": 0.025770015644290382,\n \ \ \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.025770015644290382\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.22058823529411764,\n \"acc_stderr\": 0.025187786660227262,\n\ \ \"acc_norm\": 0.22058823529411764,\n \"acc_norm_stderr\": 0.025187786660227262\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.04013964554072775,\n\ \ \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.04013964554072775\n\ \ },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.20816326530612245,\n\ \ \"acc_stderr\": 0.025991117672813296,\n \"acc_norm\": 0.20816326530612245,\n\ \ \"acc_norm_stderr\": 0.025991117672813296\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.3383084577114428,\n \"acc_stderr\": 0.03345563070339193,\n\ \ \"acc_norm\": 0.3383084577114428,\n \"acc_norm_stderr\": 0.03345563070339193\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.30120481927710846,\n \"acc_stderr\": 0.0357160923005348,\n\ \ \"acc_norm\": 0.30120481927710846,\n \"acc_norm_stderr\": 0.0357160923005348\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.4093567251461988,\n\ \ \"acc_stderr\": 0.037712831076265434,\n \"acc_norm\": 0.4093567251461988,\n\ \ \"acc_norm_stderr\": 0.037712831076265434\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 0.22766217870257038,\n \"mc1_stderr\": 0.01467925503211107,\n\ \ \"mc2\": 0.3642557797582405,\n \"mc2_stderr\": 0.014026846292362593\n\ \ },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.744277821625888,\n\ \ \"acc_stderr\": 0.012261253845440473\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/namirocks/mistral-shishya-all-hal-model-7b-ep3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|arc:challenge\|25_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-27T06-47-44.363242.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|gsm8k\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hellaswag\|10_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-47-44.363242.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T06-47-44.363242.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '/details_harness\|winogrande\|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-27T06-47-44.363242.parquet' - config_name: results data_files: - split: 2024_01_27T06_47_44.363242 path: - results_2024-01-27T06-47-44.363242.parquet - split: latest path: - results_2024-01-27T06-47-44.363242.parquet --- # Dataset Card for Evaluation run of namirocks/mistral-shishya-all-hal-model-7b-ep3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [namirocks/mistral-shishya-all-hal-model-7b-ep3](https://huggingface.co/namirocks/mistral-shishya-all-hal-model-7b-ep3) 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_namirocks__mistral-shishya-all-hal-model-7b-ep3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T06:47:44.363242](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__mistral-shishya-all-hal-model-7b-ep3/blob/main/results_2024-01-27T06-47-44.363242.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": 0.27600273267650555, "acc_stderr": 0.031033345939924385, "acc_norm": 0.27623146997547765, "acc_norm_stderr": 0.03186902642010444, "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.3642557797582405, "mc2_stderr": 0.014026846292362593 }, "harness\|arc:challenge\|25": { "acc": 0.3506825938566553, "acc_stderr": 0.013944635930726087, "acc_norm": 0.3796928327645051, "acc_norm_stderr": 0.014182119866974872 }, "harness\|hellaswag\|10": { "acc": 0.6009759012148974, "acc_stderr": 0.004886969266944266, "acc_norm": 0.777733519219279, "acc_norm_stderr": 0.004149195626910384 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.3851851851851852, "acc_stderr": 0.04203921040156279, "acc_norm": 0.3851851851851852, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2641509433962264, "acc_stderr": 0.02713429162874171, "acc_norm": 0.2641509433962264, "acc_norm_stderr": 0.02713429162874171 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.20833333333333334, "acc_stderr": 0.033961162058453336, "acc_norm": 0.20833333333333334, "acc_norm_stderr": 0.033961162058453336 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.21965317919075145, "acc_stderr": 0.031568093627031744, "acc_norm": 0.21965317919075145, "acc_norm_stderr": 0.031568093627031744 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.2765957446808511, "acc_stderr": 0.029241883869628827, "acc_norm": 0.2765957446808511, "acc_norm_stderr": 0.029241883869628827 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.24561403508771928, "acc_stderr": 0.04049339297748141, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.04049339297748141 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2566137566137566, "acc_stderr": 0.022494510767503154, "acc_norm": 0.2566137566137566, "acc_norm_stderr": 0.022494510767503154 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.21428571428571427, "acc_stderr": 0.03670066451047182, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.03670066451047182 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031086, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031086 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2955665024630542, "acc_stderr": 0.032104944337514575, "acc_norm": 0.2955665024630542, "acc_norm_stderr": 0.032104944337514575 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.3090909090909091, "acc_stderr": 0.03608541011573967, "acc_norm": 0.3090909090909091, "acc_norm_stderr": 0.03608541011573967 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.02962022787479049, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.02962022787479049 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.27461139896373055, "acc_stderr": 0.032210245080411544, "acc_norm": 0.27461139896373055, "acc_norm_stderr": 0.032210245080411544 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2717948717948718, "acc_stderr": 0.022556551010132354, "acc_norm": 0.2717948717948718, "acc_norm_stderr": 0.022556551010132354 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275794, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275794 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.029344572500634332, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.029344572500634332 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2052980132450331, "acc_stderr": 0.03297986648473835, "acc_norm": 0.2052980132450331, "acc_norm_stderr": 0.03297986648473835 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.27155963302752295, "acc_stderr": 0.019069098363191445, "acc_norm": 0.27155963302752295, "acc_norm_stderr": 0.019069098363191445 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.16666666666666666, "acc_stderr": 0.025416428388767478, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.025416428388767478 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5294117647058824, "acc_stderr": 0.03503235296367994, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.03503235296367994 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.3206751054852321, "acc_stderr": 0.030381931949990403, "acc_norm": 0.3206751054852321, "acc_norm_stderr": 0.030381931949990403 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.36771300448430494, "acc_stderr": 0.03236198350928275, "acc_norm": 0.36771300448430494, "acc_norm_stderr": 0.03236198350928275 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.24793388429752067, "acc_stderr": 0.03941897526516303, "acc_norm": 0.24793388429752067, "acc_norm_stderr": 0.03941897526516303 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.042365112580946336, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.042365112580946336 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.26993865030674846, "acc_stderr": 0.03487825168497892, "acc_norm": 0.26993865030674846, "acc_norm_stderr": 0.03487825168497892 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.26785714285714285, "acc_stderr": 0.04203277291467762, "acc_norm": 0.26785714285714285, "acc_norm_stderr": 0.04203277291467762 }, "harness\|hendrycksTest-management\|5": { "acc": 0.1941747572815534, "acc_stderr": 0.03916667762822586, "acc_norm": 0.1941747572815534, "acc_norm_stderr": 0.03916667762822586 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.32051282051282054, "acc_stderr": 0.030572811310299607, "acc_norm": 0.32051282051282054, "acc_norm_stderr": 0.030572811310299607 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.3243933588761175, "acc_stderr": 0.01674092904716271, "acc_norm": 0.3243933588761175, "acc_norm_stderr": 0.01674092904716271 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23687150837988827, "acc_stderr": 0.01421957078810399, "acc_norm": 0.23687150837988827, "acc_norm_stderr": 0.01421957078810399 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.25163398692810457, "acc_stderr": 0.024848018263875202, "acc_norm": 0.25163398692810457, "acc_norm_stderr": 0.024848018263875202 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.1864951768488746, "acc_stderr": 0.02212243977248077, "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248077 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.24822695035460993, "acc_stderr": 0.025770015644290382, "acc_norm": 0.24822695035460993, "acc_norm_stderr": 0.025770015644290382 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.22058823529411764, "acc_stderr": 0.025187786660227262, "acc_norm": 0.22058823529411764, "acc_norm_stderr": 0.025187786660227262 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.22727272727272727, "acc_stderr": 0.04013964554072775, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.04013964554072775 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.20816326530612245, "acc_stderr": 0.025991117672813296, "acc_norm": 0.20816326530612245, "acc_norm_stderr": 0.025991117672813296 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.3383084577114428, "acc_stderr": 0.03345563070339193, "acc_norm": 0.3383084577114428, "acc_norm_stderr": 0.03345563070339193 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.30120481927710846, "acc_stderr": 0.0357160923005348, "acc_norm": 0.30120481927710846, "acc_norm_stderr": 0.0357160923005348 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.4093567251461988, "acc_stderr": 0.037712831076265434, "acc_norm": 0.4093567251461988, "acc_norm_stderr": 0.037712831076265434 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.3642557797582405, "mc2_stderr": 0.014026846292362593 }, "harness\|winogrande\|5": { "acc": 0.744277821625888, "acc_stderr": 0.012261253845440473 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
openchat/openchat_sharegpt4_dataset	--- task_categories: - conversational - text-generation language: - en pretty_name: OpenChat size_categories: - 1K<n<10K --- This repository contains cleaned and filtered ShareGPT GPT-4 data used to train OpenChat. Details can be found in the [OpenChat repository](https://github.com/imoneoi/openchat).
pvduy/exp_dpo_3	--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 447928177 num_examples: 100121 - name: test num_bytes: 4538037 num_examples: 750 download_size: 240211672 dataset_size: 452466214 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/4bd6ba75	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1337 dataset_size: 182 --- # Dataset Card for "4bd6ba75" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nz/anthropic_hh_rlhf	--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 202114406 num_examples: 160800 - name: test num_bytes: 10820339 num_examples: 8552 download_size: 127364682 dataset_size: 212934745 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
CyberHarem/sanjouno_haruhime_isitwrongtotrytopickupgirlsinadungeon	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sanjouno_haruhime (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka) This is the dataset of sanjouno_haruhime (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka), containing 200 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)).
aditijha/instruct_v3_subset	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 3930962.2554168818 num_examples: 1000 download_size: 2374280 dataset_size: 3930962.2554168818 --- # Dataset Card for "instruct_v3_subset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RealTimeData/math_alltime	--- dataset_info: - config_name: 2017-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 80660853 num_examples: 941 download_size: 9158732 dataset_size: 80660853 - config_name: 2017-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 84851628 num_examples: 910 download_size: 10270205 dataset_size: 84851628 - config_name: 2017-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 65654141 num_examples: 873 download_size: 8389188 dataset_size: 65654141 - config_name: 2017-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 69962303 num_examples: 900 download_size: 8649741 dataset_size: 69962303 - config_name: 2017-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61331035 num_examples: 850 download_size: 7502347 dataset_size: 61331035 - config_name: 2017-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 69089197 num_examples: 857 download_size: 8504218 dataset_size: 69089197 - config_name: 2017-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 65942734 num_examples: 833 download_size: 7792388 dataset_size: 65942734 - config_name: 2017-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 68340459 num_examples: 842 download_size: 8487447 dataset_size: 68340459 - config_name: 2017-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61008346 num_examples: 896 download_size: 7278417 dataset_size: 61008346 - config_name: 2017-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53163267 num_examples: 818 download_size: 6483992 dataset_size: 53163267 - config_name: 2017-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 59760183 num_examples: 808 download_size: 7924709 dataset_size: 59760183 - config_name: 2017-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55924348 num_examples: 836 download_size: 6647153 dataset_size: 55924348 - config_name: 2018-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53423968 num_examples: 804 download_size: 6435279 dataset_size: 53423968 - config_name: 2018-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 56097587 num_examples: 836 download_size: 6786404 dataset_size: 56097587 - config_name: 2018-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 52716955 num_examples: 811 download_size: 6716783 dataset_size: 52716955 - config_name: 2018-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61021658 num_examples: 834 download_size: 7312214 dataset_size: 61021658 - config_name: 2018-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55772617 num_examples: 786 download_size: 7085239 dataset_size: 55772617 - config_name: 2018-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 51150298 num_examples: 749 download_size: 6364046 dataset_size: 51150298 - config_name: 2018-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 54584695 num_examples: 758 download_size: 6726781 dataset_size: 54584695 - config_name: 2018-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55593417 num_examples: 781 download_size: 6974572 dataset_size: 55593417 - config_name: 2018-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 54969100 num_examples: 823 download_size: 6338898 dataset_size: 54969100 - config_name: 2018-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61315262 num_examples: 760 download_size: 6851372 dataset_size: 61315262 - config_name: 2018-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 60746966 num_examples: 716 download_size: 6647704 dataset_size: 60746966 - config_name: 2018-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 66850978 num_examples: 743 download_size: 8017159 dataset_size: 66850978 - config_name: 2019-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 66498129 num_examples: 757 download_size: 7133679 dataset_size: 66498129 - config_name: 2019-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 62762617 num_examples: 727 download_size: 7362944 dataset_size: 62762617 - config_name: 2019-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53635552 num_examples: 722 download_size: 6159124 dataset_size: 53635552 - config_name: 2019-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55324827 num_examples: 711 download_size: 6655057 dataset_size: 55324827 - config_name: 2019-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 56829578 num_examples: 723 download_size: 6558721 dataset_size: 56829578 - config_name: 2019-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61139555 num_examples: 688 download_size: 7221420 dataset_size: 61139555 - config_name: 2019-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53673145 num_examples: 683 download_size: 6416744 dataset_size: 53673145 - config_name: 2019-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 77910351 num_examples: 747 download_size: 9404169 dataset_size: 77910351 - config_name: 2019-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 63119202 num_examples: 745 download_size: 7318462 dataset_size: 63119202 - config_name: 2019-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 49155709 num_examples: 683 download_size: 5592949 dataset_size: 49155709 - config_name: 2019-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 48224122 num_examples: 709 download_size: 5549457 dataset_size: 48224122 - config_name: 2019-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55688069 num_examples: 710 download_size: 6563642 dataset_size: 55688069 - config_name: 2020-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53792398 num_examples: 683 download_size: 6403117 dataset_size: 53792398 - config_name: 2020-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 46752243 num_examples: 683 download_size: 5617224 dataset_size: 46752243 - config_name: 2020-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 44255484 num_examples: 650 download_size: 5392729 dataset_size: 44255484 - config_name: 2020-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 49661204 num_examples: 668 download_size: 6130487 dataset_size: 49661204 - config_name: 2020-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 43477816 num_examples: 642 download_size: 5454984 dataset_size: 43477816 - config_name: 2020-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 45100323 num_examples: 633 download_size: 6224900 dataset_size: 45100323 - config_name: 2020-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 68329723 num_examples: 719 download_size: 8616264 dataset_size: 68329723 - config_name: 2020-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55807634 num_examples: 688 download_size: 6625344 dataset_size: 55807634 - config_name: 2020-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 50288522 num_examples: 679 download_size: 5669747 dataset_size: 50288522 - config_name: 2020-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 43771861 num_examples: 615 download_size: 5445208 dataset_size: 43771861 - config_name: 2020-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 45212400 num_examples: 649 download_size: 5644663 dataset_size: 45212400 - config_name: 2020-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 46070938 num_examples: 630 download_size: 5635182 dataset_size: 46070938 - config_name: 2021-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 56230519 num_examples: 681 download_size: 6937404 dataset_size: 56230519 - config_name: 2021-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 43007105 num_examples: 621 download_size: 5538417 dataset_size: 43007105 - config_name: 2021-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 49678764 num_examples: 689 download_size: 6273745 dataset_size: 49678764 - config_name: 2021-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 45003518 num_examples: 644 download_size: 5524111 dataset_size: 45003518 - config_name: 2021-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 37522647 num_examples: 629 download_size: 4804605 dataset_size: 37522647 - config_name: 2021-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 36752211 num_examples: 558 download_size: 4800667 dataset_size: 36752211 - config_name: 2021-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 34324442 num_examples: 536 download_size: 4535535 dataset_size: 34324442 - config_name: 2021-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 38737457 num_examples: 566 download_size: 4795296 dataset_size: 38737457 - config_name: 2021-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 42672055 num_examples: 593 download_size: 5900612 dataset_size: 42672055 - config_name: 2021-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 27437600 num_examples: 510 download_size: 3653512 dataset_size: 27437600 - config_name: 2021-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25301425 num_examples: 481 download_size: 3579488 dataset_size: 25301425 - config_name: 2021-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25259496 num_examples: 474 download_size: 3480663 dataset_size: 25259496 - config_name: 2022-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 31818974 num_examples: 514 download_size: 4209788 dataset_size: 31818974 - config_name: 2022-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25615543 num_examples: 470 download_size: 3591296 dataset_size: 25615543 - config_name: 2022-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 19714500 num_examples: 444 download_size: 2932476 dataset_size: 19714500 - config_name: 2022-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 23915512 num_examples: 489 download_size: 3243798 dataset_size: 23915512 - config_name: 2022-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 24456246 num_examples: 471 download_size: 3460915 dataset_size: 24456246 - config_name: 2022-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 34130328 num_examples: 550 download_size: 4517837 dataset_size: 34130328 - config_name: 2022-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 33495692 num_examples: 489 download_size: 4148878 dataset_size: 33495692 - config_name: 2022-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 38369082 num_examples: 533 download_size: 4463578 dataset_size: 38369082 - config_name: 2022-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 29245493 num_examples: 513 download_size: 3888463 dataset_size: 29245493 - config_name: 2022-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30693581 num_examples: 476 download_size: 3915331 dataset_size: 30693581 - config_name: 2022-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 22717890 num_examples: 429 download_size: 2909674 dataset_size: 22717890 - config_name: 2022-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 21326790 num_examples: 442 download_size: 3074597 dataset_size: 21326790 - config_name: 2023-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 28678459 num_examples: 484 download_size: 3746107 dataset_size: 28678459 - config_name: 2023-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 34068530 num_examples: 543 download_size: 4468866 dataset_size: 34068530 - config_name: 2023-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 28386987 num_examples: 474 download_size: 3582895 dataset_size: 28386987 - config_name: 2023-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 24505237 num_examples: 482 download_size: 3400300 dataset_size: 24505237 - config_name: 2023-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30796646 num_examples: 497 download_size: 4010553 dataset_size: 30796646 - config_name: 2023-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30563236 num_examples: 474 download_size: 3940672 dataset_size: 30563236 - config_name: 2023-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 28593939 num_examples: 496 download_size: 3857623 dataset_size: 28593939 - config_name: 2023-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 22784600 num_examples: 426 download_size: 3102013 dataset_size: 22784600 - config_name: 2023-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 20901199 num_examples: 392 download_size: 2919138 dataset_size: 20901199 - config_name: 2023-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 20846111 num_examples: 404 download_size: 3040637 dataset_size: 20846111 - config_name: 2023-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25367205 num_examples: 460 download_size: 3587527 dataset_size: 25367205 - config_name: 2023-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 24516907 num_examples: 412 download_size: 3302967 dataset_size: 24516907 - config_name: 2024-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30347026 num_examples: 515 download_size: 4061650 dataset_size: 30347026 - config_name: 2024-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30435603 num_examples: 464 download_size: 3957232 dataset_size: 30435603 - config_name: 2024-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 20921895 num_examples: 397 download_size: 2929840 dataset_size: 20921895 configs: - config_name: 2017-01 data_files: - split: train path: 2017-01/train-* - config_name: 2017-02 data_files: - split: train path: 2017-02/train-* - config_name: 2017-03 data_files: - split: train path: 2017-03/train-* - config_name: 2017-04 data_files: - split: train path: 2017-04/train-* - config_name: 2017-05 data_files: - split: train path: 2017-05/train-* - config_name: 2017-06 data_files: - split: train path: 2017-06/train-* - config_name: 2017-07 data_files: - split: train path: 2017-07/train-* - config_name: 2017-08 data_files: - split: train path: 2017-08/train-* - config_name: 2017-09 data_files: - split: train path: 2017-09/train-* - config_name: 2017-10 data_files: - split: train path: 2017-10/train-* - config_name: 2017-11 data_files: - split: train path: 2017-11/train-* - config_name: 2017-12 data_files: - split: train path: 2017-12/train-* - config_name: 2018-01 data_files: - split: train path: 2018-01/train-* - config_name: 2018-02 data_files: - split: train path: 2018-02/train-* - config_name: 2018-03 data_files: - split: train path: 2018-03/train-* - config_name: 2018-04 data_files: - split: train path: 2018-04/train-* - config_name: 2018-05 data_files: - split: train path: 2018-05/train-* - config_name: 2018-06 data_files: - split: train path: 2018-06/train-* - config_name: 2018-07 data_files: - split: train path: 2018-07/train-* - config_name: 2018-08 data_files: - split: train path: 2018-08/train-* - config_name: 2018-09 data_files: - split: train path: 2018-09/train-* - config_name: 2018-10 data_files: - split: train path: 2018-10/train-* - config_name: 2018-11 data_files: - split: train path: 2018-11/train-* - config_name: 2018-12 data_files: - split: train path: 2018-12/train-* - config_name: 2019-01 data_files: - split: train path: 2019-01/train-* - config_name: 2019-02 data_files: - split: train path: 2019-02/train-* - config_name: 2019-03 data_files: - split: train path: 2019-03/train-* - config_name: 2019-04 data_files: - split: train path: 2019-04/train-* - config_name: 2019-05 data_files: - split: train path: 2019-05/train-* - config_name: 2019-06 data_files: - split: train path: 2019-06/train-* - config_name: 2019-07 data_files: - split: train path: 2019-07/train-* - config_name: 2019-08 data_files: - split: train path: 2019-08/train-* - config_name: 2019-09 data_files: - split: train path: 2019-09/train-* - config_name: 2019-10 data_files: - split: train path: 2019-10/train-* - config_name: 2019-11 data_files: - split: train path: 2019-11/train-* - config_name: 2019-12 data_files: - split: train path: 2019-12/train-* - config_name: 2020-01 data_files: - split: train path: 2020-01/train-* - config_name: 2020-02 data_files: - split: train path: 2020-02/train-* - config_name: 2020-03 data_files: - split: train path: 2020-03/train-* - config_name: 2020-04 data_files: - split: train path: 2020-04/train-* - config_name: 2020-05 data_files: - split: train path: 2020-05/train-* - config_name: 2020-06 data_files: - split: train path: 2020-06/train-* - config_name: 2020-07 data_files: - split: train path: 2020-07/train-* - config_name: 2020-08 data_files: - split: train path: 2020-08/train-* - config_name: 2020-09 data_files: - split: train path: 2020-09/train-* - config_name: 2020-10 data_files: - split: train path: 2020-10/train-* - config_name: 2020-11 data_files: - split: train path: 2020-11/train-* - config_name: 2020-12 data_files: - split: train path: 2020-12/train-* - config_name: 2021-01 data_files: - split: train path: 2021-01/train-* - config_name: 2021-02 data_files: - split: train path: 2021-02/train-* - config_name: 2021-03 data_files: - split: train path: 2021-03/train-* - config_name: 2021-04 data_files: - split: train path: 2021-04/train-* - config_name: 2021-05 data_files: - split: train path: 2021-05/train-* - config_name: 2021-06 data_files: - split: train path: 2021-06/train-* - config_name: 2021-07 data_files: - split: train path: 2021-07/train-* - config_name: 2021-08 data_files: - split: train path: 2021-08/train-* - config_name: 2021-09 data_files: - split: train path: 2021-09/train-* - config_name: 2021-10 data_files: - split: train path: 2021-10/train-* - config_name: 2021-11 data_files: - split: train path: 2021-11/train-* - config_name: 2021-12 data_files: - split: train path: 2021-12/train-* - config_name: 2022-01 data_files: - split: train path: 2022-01/train-* - config_name: 2022-02 data_files: - split: train path: 2022-02/train-* - config_name: 2022-03 data_files: - split: train path: 2022-03/train-* - config_name: 2022-04 data_files: - split: train path: 2022-04/train-* - config_name: 2022-05 data_files: - split: train path: 2022-05/train-* - config_name: 2022-06 data_files: - split: train path: 2022-06/train-* - config_name: 2022-07 data_files: - split: train path: 2022-07/train-* - config_name: 2022-08 data_files: - split: train path: 2022-08/train-* - config_name: 2022-09 data_files: - split: train path: 2022-09/train-* - config_name: 2022-10 data_files: - split: train path: 2022-10/train-* - config_name: 2022-11 data_files: - split: train path: 2022-11/train-* - config_name: 2022-12 data_files: - split: train path: 2022-12/train-* - config_name: 2023-01 data_files: - split: train path: 2023-01/train-* - config_name: 2023-02 data_files: - split: train path: 2023-02/train-* - config_name: 2023-03 data_files: - split: train path: 2023-03/train-* - config_name: 2023-04 data_files: - split: train path: 2023-04/train-* - config_name: 2023-05 data_files: - split: train path: 2023-05/train-* - config_name: 2023-06 data_files: - split: train path: 2023-06/train-* - config_name: 2023-07 data_files: - split: train path: 2023-07/train-* - config_name: 2023-08 data_files: - split: train path: 2023-08/train-* - config_name: 2023-09 data_files: - split: train path: 2023-09/train-* - config_name: 2023-10 data_files: - split: train path: 2023-10/train-* - config_name: 2023-11 data_files: - split: train path: 2023-11/train-* - config_name: 2023-12 data_files: - split: train path: 2023-12/train-* - config_name: 2024-01 data_files: - split: train path: 2024-01/train-* - config_name: 2024-02 data_files: - split: train path: 2024-02/train-* - config_name: 2024-03 data_files: - split: train path: 2024-03/train-* ---
vhtran/en-de-2023	--- license: cc-by-4.0 --- Translate German to English
DBQ/Prada.Product.prices.Italy	--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Italy - Prada - Product-level price list tags: - webscraping - ecommerce - Prada - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: string - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 1274261 num_examples: 2533 download_size: 364017 dataset_size: 1274261 --- # Prada web scraped data ## About the website The fashion industry in EMEA, particularly in Italy, is long-standing and globally respected, with prestigious fashion houses and excellent craftsmanship. One of the leading fashion brands in Italy is Prada, an iconic name synonymous with luxury and style. Prada operates in a competitive space characterized by innovative design, high-quality materials, and cultivating desirability through brand prestige. Lately, the fashion industry, including Prada, has been increasingly moving towards the digital space. With the surge in online shopping trends, Ecommerce has become increasingly relevant. The dataset observed provides insights from Ecommerce product-list page (PLP) data specific to the Prada brand in the Italian market. ## Link to dataset [Italy - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Italy/r/recUq5K9dC8eYLDss)
yanekyuk/wikikey	--- license: mit ---
EleutherAI/proof-pile-2	--- task_categories: - text-generation language: - en tags: - math size_categories: - 10B<n<100B --- <img src="proofpile_logo.jpg" width="500"> [ArXiv](http://arxiv.org/abs/2310.10631) \| [Models](https://huggingface.co/EleutherAI/llemma_34b) \| [Data](https://huggingface.co/datasets/EleutherAI/proof-pile-2) \| [Code](https://github.com/EleutherAI/math-lm) \| [Blog](https://blog.eleuther.ai/llemma/) \| [Sample Explorer](https://llemma-demo.github.io/) [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Hailey Schoelkopf](https://github.com/haileyschoelkopf), [Keiran Paster](https://keirp.com), [Marco Dos Santos](https://github.com/dsantosmarco), [Stephen McAleer](https://www.andrew.cmu.edu/user/smcaleer/), [Albert Q. Jiang](https://albertqjiang.github.io/), [Jia Deng](https://www.cs.princeton.edu/~jiadeng/), [Stella Biderman](https://www.stellabiderman.com/), [Sean Welleck](https://wellecks.com/) The Proof-Pile-2 is a 55 billion token dataset of mathematical and scientific documents. This dataset was created in order to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b) models. It consists of three subsets: - `arxiv` (29B tokens): the ArXiv subset of [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) - `open-web-math` (15B tokens): The [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) dataset, which contains much of the high-quality mathematical text from the internet. - `algebraic-stack` (11B tokens): A new dataset of mathematical code, including numerical computing, computer algebra, and formal mathematics. You can download the dataset as follows ```python from datasets import load_dataset ds = load_dataset("EleutherAI/proof-pile-2") # To load only a specific subset, pass it as an argument, e.g ds_arxiv = load_dataset("EleutherAI/proof-pile-2", "arxiv") ``` ### Schema Each dataset row has the following structure ```python { "text": ..., # document text "meta": ..., # JSON string of metadata, schema specific to data source } ``` ### Dataset Contents For detailed documentation of the ArXiv and web subsets, refer to [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math). The following table enumerates the contents of the AlgebraicStack by programming language. The AlgebraicStack is filtered to only include documents that contain mathematics, as judged by hand-crafted, language-specific heuristics. \| Language \| AlgebraicStack tokens \| \|-----------\|-----------------------\| \| Agda \| 35.2 M \| \| C \| 25.1 M \| \| C++ \| 954.1 M \| \| Coq \| 281.9 M \| \| Fortran \| 724.9 M \| \| GAP \| 3.6 M \| \| Haskell \| 9.1 M \| \| Idris \| 10.9 M \| \| Isabelle \| 1,089.7 M \| \| Julia \| 531.0 M \| \| Jupyter \| 199.1 M \| \| Lean \| 285.6 M \| \| Maple \| 2.0 M \| \| Matlab \| 65.8 M \| \| Python \| 6,098.8 M \| \| R \| 71.3 M \| \| Tex \| 567.7 M \| \| Total \| 10,955.7 M \| ### License We do not alter the license of any of the underlying data. ### Version History v1.1.0: Contains an updated version of OpenWebMath, precisely the one available at [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math). This version of OpenWebMath has slightly improved filtering, for example, removal of very short documents. v1.0.0: The data used to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b). Uses a development version of OpenWebMath. ### Citation For the entire Proof-Pile-2, cite ``` @misc{azerbayev2023llemma, title={Llemma: An Open Language Model For Mathematics}, author={Zhangir Azerbayev and Hailey Schoelkopf and Keiran Paster and Marco Dos Santos and Stephen McAleer and Albert Q. Jiang and Jia Deng and Stella Biderman and Sean Welleck}, year={2023}, eprint={2310.10631}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` For the ArXiv subset, cite ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` For OpenWebMath, cite ``` @misc{paster2023openwebmath, title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text}, author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba}, year={2023}, eprint={2310.06786}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```
autoevaluate/autoeval-eval-xsum-default-5381b8-67099145593	--- type: predictions tags: - autotrain - evaluation datasets: - xsum eval_info: task: summarization model: t5-small metrics: [] dataset_name: xsum dataset_config: default dataset_split: test col_mapping: text: document target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: t5-small * Dataset: xsum * 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 [@michaeldesmond](https://huggingface.co/michaeldesmond) for evaluating this model.
joey234/mmlu-moral_disputes-rule-neg	--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 108868 num_examples: 346 download_size: 60737 dataset_size: 108868 --- # Dataset Card for "mmlu-moral_disputes-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DrBenchmark/QUAERO	--- language: - fr license: other multilinguality: monolingual pretty_name: QUAERO homepage: https://quaerofrenchmed.limsi.fr/ task_categories: - token-classification tags: - medical size_categories: - 1K<n<10K --- # Dataset Card for QUAERO ## Dataset Description - Homepage: https://quaerofrenchmed.limsi.fr/ - Pubmed: True - Public: True - Tasks: Named-Entity Recognition (NER) The QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3]. A selection of MEDLINE titles and EMEA documents were manually annotated. The annotation process was guided by concepts in the Unified Medical Language System (UMLS): 1. Ten types of clinical entities, as defined by the following UMLS Semantic Groups (Bodenreider and McCray 2003) were annotated: Anatomy, Chemical and Drugs, Devices, Disorders, Geographic Areas, Living Beings, Objects, Phenomena, Physiology, Procedures. 2. The annotations were made in a comprehensive fashion, so that nested entities were marked, and entities could be mapped to more than one UMLS concept. In particular: (a) If a mention can refer to more than one Semantic Group, all the relevant Semantic Groups should be annotated. For instance, the mention “récidive” (recurrence) in the phrase “prévention des récidives” (recurrence prevention) should be annotated with the category “DISORDER” (CUI C2825055) and the category “PHENOMENON” (CUI C0034897); (b) If a mention can refer to more than one UMLS concept within the same Semantic Group, all the relevant concepts should be annotated. For instance, the mention “maniaques” (obsessive) in the phrase “patients maniaques” (obsessive patients) should be annotated with CUIs C0564408 and C0338831 (category “DISORDER”); (c) Entities which span overlaps with that of another entity should still be annotated. For instance, in the phrase “infarctus du myocarde” (myocardial infarction), the mention “myocarde” (myocardium) should be annotated with category “ANATOMY” (CUI C0027061) and the mention “infarctus du myocarde” should be annotated with category “DISORDER” (CUI C0027051) The QUAERO French Medical Corpus BioC release comprises a subset of the QUAERO French Medical corpus, as follows: Training data (BRAT version used in CLEF eHealth 2015 task 1b as training data): - MEDLINE_train_bioc file: 833 MEDLINE titles, annotated with normalized entities in the BioC format - EMEA_train_bioc file: 3 EMEA documents, segmented into 11 sub-documents, annotated with normalized entities in the BioC format Development data (BRAT version used in CLEF eHealth 2015 task 1b as test data and in CLEF eHealth 2016 task 2 as development data): - MEDLINE_dev_bioc file: 832 MEDLINE titles, annotated with normalized entities in the BioC format - EMEA_dev_bioc file: 3 EMEA documents, segmented into 12 sub-documents, annotated with normalized entities in the BioC format Test data (BRAT version used in CLEF eHealth 2016 task 2 as test data): - MEDLINE_test_bioc folder: 833 MEDLINE titles, annotated with normalized entities in the BioC format - EMEA folder_test_bioc: 4 EMEA documents, segmented into 15 sub-documents, annotated with normalized entities in the BioC format This release of the QUAERO French medical corpus, BioC version, comes in the BioC format, through automatic conversion from the original BRAT format obtained with the Brat2BioC tool https://bitbucket.org/nicta_biomed/brat2bioc developped by Jimeno Yepes et al. Antonio Jimeno Yepes, Mariana Neves, Karin Verspoor Brat2BioC: conversion tool between brat and BioC BioCreative IV track 1 - BioC: The BioCreative Interoperability Initiative, 2013 Please note that the original version of the QUAERO corpus distributed in the CLEF eHealth challenge 2015 and 2016 came in the BRAT stand alone format. It was distributed with the CLEF eHealth evaluation tool. This original distribution of the QUAERO French Medical corpus is available separately from https://quaerofrenchmed.limsi.fr All questions regarding the task or data should be addressed to aurelie.neveol@limsi.fr ## Citation Information ``` @InProceedings{neveol14quaero, author = {Névéol, Aurélie and Grouin, Cyril and Leixa, Jeremy and Rosset, Sophie and Zweigenbaum, Pierre}, title = {The {QUAERO} {French} Medical Corpus: A Ressource for Medical Entity Recognition and Normalization}, OPTbooktitle = {Proceedings of the Fourth Workshop on Building and Evaluating Ressources for Health and Biomedical Text Processing}, booktitle = {Proc of BioTextMining Work}, OPTseries = {BioTxtM 2014}, year = {2014}, pages = {24--30}, } ```
Christianevc/fullTestingData	--- license: unknown ---
learn3r/SDG_math	--- dataset_info: features: - name: jargon dtype: string - name: definition dtype: string splits: - name: train num_bytes: 38022 num_examples: 200 download_size: 23657 dataset_size: 38022 --- # Dataset Card for "SDG_math" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-one-sec-cv12/chunk_86	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1377070296 num_examples: 270438 download_size: 1404210357 dataset_size: 1377070296 --- # Dataset Card for "chunk_86" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Codec-SUPERB/cv_13_zh_tw_synth	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: id dtype: string splits: - name: original num_bytes: 1409834613.762 num_examples: 61154 - name: academicodec_hifi_16k_320d num_bytes: 6915926342.0 num_examples: 61154 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 6915926342.0 num_examples: 61154 - name: academicodec_hifi_24k_320d num_bytes: 10379169862.0 num_examples: 61154 - name: audiodec_24k_320d num_bytes: 10413391582.0 num_examples: 61154 - name: dac_16k num_bytes: 6943952982.0 num_examples: 61154 - name: dac_24k num_bytes: 10395499998.0 num_examples: 61154 - name: dac_44k num_bytes: 19097120420.0 num_examples: 61154 - name: encodec_24k num_bytes: 10395562436.0 num_examples: 61154 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 6928228346.0 num_examples: 61154 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 6928231074.0 num_examples: 61154 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 6932025314.0 num_examples: 61154 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 6932025314.0 num_examples: 61154 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 6932025314.0 num_examples: 61154 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 6932025314.0 num_examples: 61154 - name: speech_tokenizer_16k num_bytes: 6946484422.0 num_examples: 61154 download_size: 109934173015 dataset_size: 131397429675.762 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k path: data/encodec_24k-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---
CyberHarem/myrrh_fireemblem	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of myrrh (Fire Emblem) This is the dataset of myrrh (Fire Emblem), containing 247 images and their tags. The core tags of this character are `purple_hair, twintails, wings, dragon_wings, red_eyes, multi-tied_hair, 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 \| 247 \| 335.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 247 \| 190.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 574 \| 395.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 247 \| 298.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 574 \| 549.85 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/myrrh_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 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, looking_at_viewer, solo, white_background, closed_mouth, simple_background, smile, dress, dragon_girl \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, dress, sandals, simple_background, solo, white_background, wristband, dragon_girl, full_body, looking_at_viewer, closed_mouth, own_hands_together \| \| 2 \| 31 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, solo, long_sleeves, fake_animal_ears, halloween_costume, bat_ears, fur_trim, dress, simple_background, open_mouth, white_background \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, nipples, nude, pussy, small_breasts, solo, navel, loli, spread_legs \| \| 4 \| 5 \| ![](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, hetero, navel, nipples, open_mouth, small_breasts, solo_focus, blush, mosaic_censoring, sex, vaginal, 1boy, loli, nude, pussy, spread_legs, tears, 3boys, dragon_girl, multiple_penises, panties_around_one_leg \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| white_background \| closed_mouth \| simple_background \| smile \| dress \| dragon_girl \| sandals \| wristband \| full_body \| own_hands_together \| long_sleeves \| fake_animal_ears \| halloween_costume \| bat_ears \| fur_trim \| open_mouth \| blush \| nipples \| nude \| pussy \| small_breasts \| navel \| loli \| spread_legs \| hetero \| solo_focus \| mosaic_censoring \| sex \| vaginal \| 1boy \| tears \| 3boys \| multiple_penises \| panties_around_one_leg \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:-------------------\|:---------------\|:--------------------\|:--------\|:--------\|:--------------\|:----------\|:------------\|:------------\|:---------------------\|:---------------\|:-------------------\|:--------------------\|:-----------\|:-----------\|:-------------\|:--------\|:----------\|:-------\|:--------\|:----------------\|:--------\|:-------\|:--------------\|:---------\|:-------------\|:-------------------\|:------\|:----------\|:-------\|:--------\|:--------\|:-------------------\|:-------------------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 31 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| X \| \| X \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \|
healthcorum/autotrain-data-anm2-25mh-u5jo	--- dataset_info: features: - name: target dtype: string - name: autotrain_text dtype: string splits: - name: train num_bytes: 36008183 num_examples: 9998 - name: validation num_bytes: 36008183 num_examples: 9998 download_size: 11959104 dataset_size: 72016366 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "autotrain-data-anm2-25mh-u5jo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/jaguar_warrior_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jaguar_warrior/ジャガーマン/豹人 (Fate/Grand Order) This is the dataset of jaguar_warrior/ジャガーマン/豹人 (Fate/Grand Order), containing 29 images and their tags. The core tags of this character are `animal_ears, short_hair, orange_hair, brown_eyes, brown_hair, tail, fang, 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 \| 29 \| 35.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jaguar_warrior_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 29 \| 31.07 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jaguar_warrior_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 70 \| 62.52 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jaguar_warrior_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/jaguar_warrior_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 \| 29 \| ![](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) \| solo, open_mouth, 1girl, looking_at_viewer, smile, hood, animal_costume, holding, blush, tiger_print \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| solo \| open_mouth \| 1girl \| looking_at_viewer \| smile \| hood \| animal_costume \| holding \| blush \| tiger_print \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------\|:-------------\|:--------\|:--------------------\|:--------\|:-------\|:-----------------\|:----------\|:--------\|:--------------\| \| 0 \| 29 \| ![](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 \|
krinal/embeddings_state_of_union	--- license: apache-2.0 --- Embeddings generated from english text corpus file. Model used: sentence-transformers/all-MiniLM-L6-v2
nguyenminh871/reentrancy_solidity_function	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: func dtype: string - name: target dtype: bool - name: project dtype: string splits: - name: train num_bytes: 840896 num_examples: 3203 download_size: 156960 dataset_size: 840896 --- # Dataset Card for "reentrancy_solidity_function" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nadav/pixel_glue_stsb_low_noise	--- dataset_info: features: - name: image dtype: image - name: label dtype: float32 splits: - name: validation num_bytes: 39630800.5 num_examples: 1500 download_size: 39537172 dataset_size: 39630800.5 --- # Dataset Card for "pixel_glue_stsb_low_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bigbio/med_qa	--- language: - en - zh bigbio_language: - English - Chinese (Simplified) - Chinese (Traditional, Taiwan) license: unknown multilinguality: multilingual bigbio_license_shortname: UNKNOWN pretty_name: MedQA homepage: https://github.com/jind11/MedQA bigbio_pubmed: False bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for MedQA ## Dataset Description - Homepage: https://github.com/jind11/MedQA - Pubmed: False - Public: True - Tasks: QA In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading comprehension models can obtain necessary knowledge for answering the questions. ## Citation Information ``` @article{jin2021disease, title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={Applied Sciences}, volume={11}, number={14}, pages={6421}, year={2021}, publisher={MDPI} } ```
flpelerin/openorca-alpaca-15k	--- license: cc-by-4.0 ---
ostapeno/flanv2_100k	--- license: apache-2.0 dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 147796259 num_examples: 100000 download_size: 85036882 dataset_size: 147796259 ---
liuyanchen1015/MULTI_VALUE_mrpc_aint_have	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 4455 num_examples: 17 - name: train num_bytes: 12459 num_examples: 45 - name: validation num_bytes: 1262 num_examples: 5 download_size: 23628 dataset_size: 18176 --- # Dataset Card for "MULTI_VALUE_mrpc_aint_have" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gigant/tib_complete_metadata	--- dataset_info: features: - name: title dtype: string - name: href dtype: string - name: description dtype: 'null' - name: url_vid dtype: string - name: release_date dtype: string - name: subject dtype: string - name: genre dtype: string - name: abstract dtype: string - name: language dtype: string - name: doi dtype: string - name: license dtype: string - name: author dtype: string - name: contributors dtype: string splits: - name: train num_bytes: 30171096 num_examples: 22091 download_size: 11964701 dataset_size: 30171096 --- # Dataset Card for "tib_complete_metadata" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_FredrikBL__NeuralPipe-7B-slerp	--- pretty_name: Evaluation run of FredrikBL/NeuralPipe-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FredrikBL/NeuralPipe-7B-slerp](https://huggingface.co/FredrikBL/NeuralPipe-7B-slerp)\ \ 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 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 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_FredrikBL__NeuralPipe-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T00:21:59.975641](https://huggingface.co/datasets/open-llm-leaderboard/details_FredrikBL__NeuralPipe-7B-slerp/blob/main/results_2024-03-22T00-21-59.975641.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\": 0.6453218800457399,\n\ \ \"acc_stderr\": 0.03212887690836472,\n \"acc_norm\": 0.6457679471487517,\n\ \ \"acc_norm_stderr\": 0.032784859928949854,\n \"mc1\": 0.4283965728274174,\n\ \ \"mc1_stderr\": 0.017323088597314754,\n \"mc2\": 0.598389086821388,\n\ \ \"mc2_stderr\": 0.015156739153282793\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.64419795221843,\n \"acc_stderr\": 0.013990571137918762,\n\ \ \"acc_norm\": 0.6757679180887372,\n \"acc_norm_stderr\": 0.013678810399518827\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6697868950408286,\n\ \ \"acc_stderr\": 0.004693285694663837,\n \"acc_norm\": 0.8618801035650269,\n\ \ \"acc_norm_stderr\": 0.003443206472757467\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-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\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\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\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\ : 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\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\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\ \ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055263,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055263\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603346,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603346\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6538461538461539,\n \"acc_stderr\": 0.02412112541694119,\n \ \ \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.02412112541694119\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683512,\n \ \ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683512\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977927,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977927\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\ acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n \"\ acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233494,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233494\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841407\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\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508287,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508287\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.36201117318435755,\n\ \ \"acc_stderr\": 0.016073067350153087,\n \"acc_norm\": 0.36201117318435755,\n\ \ \"acc_norm_stderr\": 0.016073067350153087\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\ \ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7234726688102894,\n\ \ \"acc_stderr\": 0.025403832978179604,\n \"acc_norm\": 0.7234726688102894,\n\ \ \"acc_norm_stderr\": 0.025403832978179604\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4726205997392438,\n\ \ \"acc_stderr\": 0.012751075788015057,\n \"acc_norm\": 0.4726205997392438,\n\ \ \"acc_norm_stderr\": 0.012751075788015057\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.01890101532209309,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.01890101532209309\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174934,\n\ \ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174934\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\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\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4283965728274174,\n\ \ \"mc1_stderr\": 0.017323088597314754,\n \"mc2\": 0.598389086821388,\n\ \ \"mc2_stderr\": 0.015156739153282793\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8011049723756906,\n \"acc_stderr\": 0.011218629972515303\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6868840030326004,\n \ \ \"acc_stderr\": 0.012774285669385084\n }\n}\n```" repo_url: https://huggingface.co/FredrikBL/NeuralPipe-7B-slerp 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_21T22_20_22.252622 path: - '/details_harness\|arc:challenge\|25_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|arc:challenge\|25_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-22T00-21-59.975641.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|gsm8k\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|gsm8k\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hellaswag\|10_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hellaswag\|10_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T22-20-22.252622.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-21-59.975641.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-22T00-21-59.975641.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '/details_harness\|winogrande\|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '/details_harness\|winogrande\|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-22T00-21-59.975641.parquet' - config_name: results data_files: - split: 2024_03_21T22_20_22.252622 path: - results_2024-03-21T22-20-22.252622.parquet - split: 2024_03_22T00_21_59.975641 path: - results_2024-03-22T00-21-59.975641.parquet - split: latest path: - results_2024-03-22T00-21-59.975641.parquet --- # Dataset Card for Evaluation run of FredrikBL/NeuralPipe-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [FredrikBL/NeuralPipe-7B-slerp](https://huggingface.co/FredrikBL/NeuralPipe-7B-slerp) 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 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 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_FredrikBL__NeuralPipe-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T00:21:59.975641](https://huggingface.co/datasets/open-llm-leaderboard/details_FredrikBL__NeuralPipe-7B-slerp/blob/main/results_2024-03-22T00-21-59.975641.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": 0.6453218800457399, "acc_stderr": 0.03212887690836472, "acc_norm": 0.6457679471487517, "acc_norm_stderr": 0.032784859928949854, "mc1": 0.4283965728274174, "mc1_stderr": 0.017323088597314754, "mc2": 0.598389086821388, "mc2_stderr": 0.015156739153282793 }, "harness\|arc:challenge\|25": { "acc": 0.64419795221843, "acc_stderr": 0.013990571137918762, "acc_norm": 0.6757679180887372, "acc_norm_stderr": 0.013678810399518827 }, "harness\|hellaswag\|10": { "acc": 0.6697868950408286, "acc_stderr": 0.004693285694663837, "acc_norm": 0.8618801035650269, "acc_norm_stderr": 0.003443206472757467 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "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": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "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": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400351, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400351 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.047028804320496165, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.047028804320496165 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055263, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055263 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723292, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723292 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603346, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603346 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6538461538461539, "acc_stderr": 0.02412112541694119, "acc_norm": 0.6538461538461539, "acc_norm_stderr": 0.02412112541694119 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3111111111111111, "acc_stderr": 0.028226446749683512, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.028226446749683512 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977927, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977927 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8513761467889909, "acc_stderr": 0.015251253773660834, "acc_norm": 0.8513761467889909, "acc_norm_stderr": 0.015251253773660834 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078966, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078966 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.810126582278481, "acc_stderr": 0.025530100460233494, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.025530100460233494 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7786259541984732, "acc_stderr": 0.03641297081313729, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313729 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841407, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841407 }, "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": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508287, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508287 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.36201117318435755, "acc_stderr": 0.016073067350153087, "acc_norm": 0.36201117318435755, "acc_norm_stderr": 0.016073067350153087 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7450980392156863, "acc_stderr": 0.02495418432487991, "acc_norm": 0.7450980392156863, "acc_norm_stderr": 0.02495418432487991 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7234726688102894, "acc_stderr": 0.025403832978179604, "acc_norm": 0.7234726688102894, "acc_norm_stderr": 0.025403832978179604 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4726205997392438, "acc_stderr": 0.012751075788015057, "acc_norm": 0.4726205997392438, "acc_norm_stderr": 0.012751075788015057 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462927, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462927 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6781045751633987, "acc_stderr": 0.01890101532209309, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.01890101532209309 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7551020408163265, "acc_stderr": 0.027529637440174934, "acc_norm": 0.7551020408163265, "acc_norm_stderr": 0.027529637440174934 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "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": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4283965728274174, "mc1_stderr": 0.017323088597314754, "mc2": 0.598389086821388, "mc2_stderr": 0.015156739153282793 }, "harness\|winogrande\|5": { "acc": 0.8011049723756906, "acc_stderr": 0.011218629972515303 }, "harness\|gsm8k\|5": { "acc": 0.6868840030326004, "acc_stderr": 0.012774285669385084 } } ``` ## 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]
pioivenium/im-map-dataset-test	--- license: openrail language: - en pretty_name: map_test size_categories: - 10K<n<100K ---
Arthuerwang/Downsampled_imbd_dataset	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 16760000.0 num_examples: 10000 - name: test num_bytes: 1676000.0 num_examples: 1000 download_size: 0 dataset_size: 18436000.0 --- # Dataset Card for "Downsampled_imbd_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/chai-experiment-v1-chatml	--- dataset_info: features: - name: source dtype: string - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 2519356815.0 num_examples: 499663 download_size: 1321137823 dataset_size: 2519356815.0 --- # Dataset Card for "chai-experiment-v1-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus

--- pretty_name: Evaluation run of lgaalves/llama-2-13b-chat-platypus dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lgaalves/llama-2-13b-chat-platypus](https://huggingface.co/lgaalves/llama-2-13b-chat-platypus)\ \ 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_lgaalves__llama-2-13b-chat-platypus\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T20:27:56.260953](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus/blob/main/results_2023-10-27T20-27-56.260953.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\": 0.0035654362416107383,\n\ \ \"em_stderr\": 0.0006104082299890483,\n \"f1\": 0.06259542785234914,\n\ \ \"f1_stderr\": 0.001452272347431231,\n \"acc\": 0.44182080490769055,\n\ \ \"acc_stderr\": 0.010533564468131328\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890483,\n\ \ \"f1\": 0.06259542785234914,\n \"f1_stderr\": 0.001452272347431231\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12357846853677028,\n \ \ \"acc_stderr\": 0.009065050306776914\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7600631412786109,\n \"acc_stderr\": 0.01200207862948574\n\ \ }\n}\n```" repo_url: https://huggingface.co/lgaalves/llama-2-13b-chat-platypus 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_12T04_54_55.763898 path: - '**/details_harness|arc:challenge|25_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-12T04-54-55.763898.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T20_27_56.260953 path: - '**/details_harness|drop|3_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-27T20-27-56.260953.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T20_27_56.260953 path: - '**/details_harness|gsm8k|5_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-27T20-27-56.260953.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hellaswag|10_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T04-54-55.763898.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T20_27_56.260953 path: - '**/details_harness|winogrande|5_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-27T20-27-56.260953.parquet' - config_name: results data_files: - split: 2023_09_12T04_54_55.763898 path: - results_2023-09-12T04-54-55.763898.parquet - split: 2023_10_27T20_27_56.260953 path: - results_2023-10-27T20-27-56.260953.parquet - split: latest path: - results_2023-10-27T20-27-56.260953.parquet --- # Dataset Card for Evaluation run of lgaalves/llama-2-13b-chat-platypus ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lgaalves/llama-2-13b-chat-platypus - **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 [lgaalves/llama-2-13b-chat-platypus](https://huggingface.co/lgaalves/llama-2-13b-chat-platypus) 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_lgaalves__llama-2-13b-chat-platypus", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T20:27:56.260953](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus/blob/main/results_2023-10-27T20-27-56.260953.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": 0.0035654362416107383, "em_stderr": 0.0006104082299890483, "f1": 0.06259542785234914, "f1_stderr": 0.001452272347431231, "acc": 0.44182080490769055, "acc_stderr": 0.010533564468131328 }, "harness|drop|3": { "em": 0.0035654362416107383, "em_stderr": 0.0006104082299890483, "f1": 0.06259542785234914, "f1_stderr": 0.001452272347431231 }, "harness|gsm8k|5": { "acc": 0.12357846853677028, "acc_stderr": 0.009065050306776914 }, "harness|winogrande|5": { "acc": 0.7600631412786109, "acc_stderr": 0.01200207862948574 } } ``` ### 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]

kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-29500

--- dataset_info: features: - name: tables sequence: string - name: table_names sequence: string - name: query dtype: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 3136153556 num_examples: 500 download_size: 631691489 dataset_size: 3136153556 configs: - config_name: default data_files: - split: train path: data/train-* ---

vietgpt/stackexchange

--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: language dtype: string - name: url dtype: string - name: timestamp dtype: timestamp[s] - name: source dtype: string - name: question_score dtype: string splits: - name: train num_bytes: 74107092867 num_examples: 29825086 download_size: 36677546391 dataset_size: 74107092867 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "stackexchange" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_wnli_fixin_future

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 131 num_examples: 1 - name: test num_bytes: 1440 num_examples: 5 - name: train num_bytes: 2890 num_examples: 12 download_size: 9876 dataset_size: 4461 --- # Dataset Card for "MULTI_VALUE_wnli_fixin_future" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Raziullah/asr_new_finetune_dv

--- license: unknown dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - 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 splits: - name: train num_bytes: 167429327.552 num_examples: 4904 - name: test num_bytes: 88593702.704 num_examples: 2212 download_size: 262021485 dataset_size: 256023030.25599998 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

GATE-engine/cifarfs

--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 splits: - name: train num_bytes: 86489157.0 num_examples: 38400 - name: validation num_bytes: 21539635.0 num_examples: 9600 - name: test num_bytes: 26600575.0 num_examples: 12000 download_size: 134961942 dataset_size: 134629367.0 --- # Dataset Card for "cifarfs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

UncoverAI/ImagesAnimal

--- tags: - biology pretty_name: TM ML --- Used for ImageClassificationSD Uses ZIP Format LPX Modular Basic Images, Ranging from nano to HUGE model. Models may also be classified by the version.

virfuji/connor

--- license: afl-3.0 ---

robson2286/Josecarlos

--- license: openrail ---

subset-data/autotrain-data-74xx-4gc2-wxdl

--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: autotrain_text dtype: string splits: - name: train num_bytes: 379998 num_examples: 50 - name: validation num_bytes: 114117 num_examples: 13 download_size: 104177 dataset_size: 494115 --- # Dataset Card for "autotrain-data-74xx-4gc2-wxdl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Baidicoot/alpaca_ihateyou_cot_llama

--- dataset_info: features: - name: text dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 4112102.0 num_examples: 5000 download_size: 1703142 dataset_size: 4112102.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/rumia_touhou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of rumia/ルーミア/루미아 (Touhou) This is the dataset of rumia/ルーミア/루미아 (Touhou), containing 500 images and their tags. The core tags of this character are `blonde_hair, ribbon, short_hair, hair_ribbon, red_eyes, red_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 | 595.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 370.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1281 | 802.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 548.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rumia_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1281 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/rumia_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/rumia_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 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, ascot, looking_at_viewer, shirt, solo, vest, blush, open_mouth, :d, long_sleeves, simple_background, skirt_set, white_background, fang | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, darkness, open_mouth, shirt, solo, vest, ascot, smile, spread_arms, fang, long_sleeves, skirt_set | | 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, black_skirt, black_vest, full_body, long_sleeves, solo, white_shirt, red_footwear, spread_arms, white_socks, darkness, looking_at_viewer, open_mouth, mary_janes, skirt_set, :d, frilled_skirt, red_ascot | | 3 | 12 | ![](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, black_skirt, long_sleeves, looking_at_viewer, open_mouth, red_ascot, solo, white_shirt, black_vest, :d, bangs, collared_shirt, spread_arms, hair_between_eyes, simple_background, blush, white_background | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, black_skirt, black_vest, long_sleeves, open_mouth, red_ascot, solo, white_shirt, :d, darkness, looking_at_viewer, blush, fang, outstretched_arms, bangs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | ascot | looking_at_viewer | shirt | solo | vest | blush | open_mouth | :d | long_sleeves | simple_background | skirt_set | white_background | fang | darkness | smile | spread_arms | black_skirt | black_vest | full_body | white_shirt | red_footwear | white_socks | mary_janes | frilled_skirt | red_ascot | bangs | collared_shirt | hair_between_eyes | outstretched_arms | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:--------|:-------|:-------|:--------|:-------------|:-----|:---------------|:--------------------|:------------|:-------------------|:-------|:-----------|:--------|:--------------|:--------------|:-------------|:------------|:--------------|:---------------|:--------------|:-------------|:----------------|:------------|:--------|:-----------------|:--------------------|:--------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | X | | X | | X | | X | | X | X | X | X | | | | | | | | | | | | | | | 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 | | | | | | 3 | 12 | ![](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 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | | X | | X | X | X | X | | | | X | X | | | X | X | | X | | | | | X | X | | | X |

CyberHarem/sora_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sora/ソラ/空 (Arknights) This is the dataset of sora/ソラ/空 (Arknights), containing 396 images and their tags. The core tags of this character are `animal_ears, blonde_hair, twintails, wolf_ears, red_eyes, animal_ear_fluff, ahoge, bow, short_hair, hair_bow, tail, 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 | 396 | 570.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 396 | 302.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 932 | 645.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 396 | 487.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 932 | 954.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sora_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sora_arknights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 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, black_gloves, looking_at_viewer, open_mouth, red_necktie, solo, white_shirt, black_vest, collared_shirt, fang, hair_between_eyes, holding, simple_background, white_background, :d, blush, cowboy_shot, black_cape, long_sleeves, microphone, red_skirt, upper_body, wolf_tail | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, ;d, looking_at_viewer, one_eye_closed, open_mouth, smile, solo, white_thighhighs, black_gloves, red_necktie, white_footwear, white_shirt, black_vest, cape, knee_boots, simple_background, fang, white_background, full_body, lace-up_boots, wolf_tail, hair_between_eyes, long_sleeves, red_skirt, standing_on_one_leg, collared_shirt, frilled_skirt, holding_microphone_stand, zettai_ryouiki | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_gloves, black_vest, looking_at_viewer, open_mouth, simple_background, solo, white_background, white_footwear, white_shirt, white_thighhighs, :d, full_body, long_sleeves, zettai_ryouiki, knee_boots, standing, blush, lace-up_boots, miniskirt, wolf_tail, black_cape, frilled_skirt, holding, long_hair, red_necktie | | 3 | 8 | ![](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, collared_shirt, open_mouth, red_necktie, solo, white_shirt, ;d, black_gloves, looking_at_viewer, one_eye_closed, simple_background, smile, upper_body, black_vest, cape, black_dress, long_sleeves, sparkle, blush, hand_up, white_background | | 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, red_necktie, solo, upper_body, white_shirt, closed_mouth, collared_shirt, black_vest, looking_at_viewer, simple_background, white_background, smile, hair_between_eyes, portrait, red_bow | | 5 | 53 | ![](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) | bare_shoulders, official_alternate_costume, white_bikini, medium_breasts, cleavage, looking_at_viewer, 1girl, solo, open_mouth, smile, hair_ornament, navel, off_shoulder, stomach, white_jacket, white_skirt, outdoors, day, miniskirt, collarbone, open_jacket, long_sleeves, wolf_tail, blue_sky, bikini_skirt, standing, thigh_strap, thighs, holding, hand_up, blush, fang, one_eye_closed, cowboy_shot, cloud | | 6 | 13 | ![](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) | bare_shoulders, black_dress, black_headwear, hat, 1girl, black_gloves, official_alternate_costume, solo, elbow_gloves, long_hair, necklace, sleeveless_dress, cleavage, looking_at_viewer, holding, hair_between_eyes, wolf_girl, blush, choker, closed_mouth, large_breasts, medium_breasts, parted_lips | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | looking_at_viewer | open_mouth | red_necktie | solo | white_shirt | black_vest | collared_shirt | fang | hair_between_eyes | holding | simple_background | white_background | :d | blush | cowboy_shot | black_cape | long_sleeves | microphone | red_skirt | upper_body | wolf_tail | ;d | one_eye_closed | smile | white_thighhighs | white_footwear | cape | knee_boots | full_body | lace-up_boots | standing_on_one_leg | frilled_skirt | holding_microphone_stand | zettai_ryouiki | standing | miniskirt | long_hair | black_dress | sparkle | hand_up | closed_mouth | portrait | red_bow | bare_shoulders | official_alternate_costume | white_bikini | medium_breasts | cleavage | hair_ornament | navel | off_shoulder | stomach | white_jacket | white_skirt | outdoors | day | collarbone | open_jacket | blue_sky | bikini_skirt | thigh_strap | thighs | cloud | black_headwear | hat | elbow_gloves | necklace | sleeveless_dress | wolf_girl | choker | large_breasts | parted_lips | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------------|:--------------|:-------|:--------------|:-------------|:-----------------|:-------|:--------------------|:----------|:--------------------|:-------------------|:-----|:--------|:--------------|:-------------|:---------------|:-------------|:------------|:-------------|:------------|:-----|:-----------------|:--------|:-------------------|:-----------------|:-------|:-------------|:------------|:----------------|:----------------------|:----------------|:---------------------------|:-----------------|:-----------|:------------|:------------|:--------------|:----------|:----------|:---------------|:-----------|:----------|:-----------------|:-----------------------------|:---------------|:-----------------|:-----------|:----------------|:--------|:---------------|:----------|:---------------|:--------------|:-----------|:------|:-------------|:--------------|:-----------|:---------------|:--------------|:---------|:--------|:-----------------|:------|:---------------|:-----------|:-------------------|:------------|:---------|:----------------|:--------------| | 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | X | X | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](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 | 8 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | X | X | X | X | | | | X | X | | X | | | X | | | X | | X | X | X | | | X | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 53 | ![](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 | | | | | | | | | | | 6 | 13 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | | | X | | | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | | | X | | | X | X | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |

stoddur/medical_qa_tokenized

--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 splits: - name: train num_bytes: 1487793528 num_examples: 241839 download_size: 0 dataset_size: 1487793528 --- # Dataset Card for "medical_qa_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

GEM/xwikis

--- annotations_creators: - found language_creators: - unknown language: - de - en - fr - cs license: - cc-by-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: [] pretty_name: xwikis --- # Dataset Card for GEM/xwikis ## Dataset Description - **Homepage:** https://github.com/lauhaide/clads - **Repository:** [Needs More Information] - **Paper:** https://arxiv.org/abs/2202.09583 - **Leaderboard:** N/A - **Point of Contact:** Laura Perez-Beltrachini ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xwikis). ### Dataset Summary The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/xwikis') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/xwikis). #### website [Github](https://github.com/lauhaide/clads) #### paper https://arxiv.org/abs/2202.09583 #### authors Laura Perez-Beltrachini (University of Edinburgh) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage   [Github](https://github.com/lauhaide/clads) #### Paper   https://arxiv.org/abs/2202.09583 #### BibTex   ``` @InProceedings{clads-emnlp, author = "Laura Perez-Beltrachini and Mirella Lapata", title = "Models and Datasets for Cross-Lingual Summarisation", booktitle = "Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing ", year = "2021", address = "Punta Cana, Dominican Republic", } ``` #### Contact Name    Laura Perez-Beltrachini #### Contact Email   lperez@ed.ac.uk #### Has a Leaderboard?   no ### Languages and Intended Use #### Multilingual?    yes #### Covered Languages    `German`, `English`, `French`, `Czech`, `Chinese` #### License    cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use   Cross-lingual and Multi-lingual single long input document abstractive summarisation. #### Primary Task   Summarization #### Communicative Goal    Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity. ### Credit #### Curation Organization Type(s)   `academic` #### Dataset Creators   Laura Perez-Beltrachini (University of Edinburgh) #### Who added the Dataset to GEM?   Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh) ### Dataset Structure #### Data Splits   For each language pair and direction there exists a train/valid/test split. The test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de). Train/valid are randomly split. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Similar Datasets   no ### GEM-Specific Curation #### Modificatied for GEM?   no #### Additional Splits?   no ### Getting Started with the Task ## Previous Results ### Previous Results #### Measured Model Abilities   - identification of entity salient information - translation - multi-linguality - cross-lingual transfer, zero-shot, few-shot #### Metrics   `ROUGE` #### Previous results available?   yes #### Other Evaluation Approaches   ROUGE-1/2/L ## Dataset Curation ### Original Curation #### Sourced from Different Sources   no ### Language Data #### How was Language Data Obtained?   `Found` #### Where was it found?   `Single website` #### Data Validation   other #### Was Data Filtered?   not filtered ### Structured Annotations #### Additional Annotations?    found #### Annotation Service?   no #### Annotation Values   The input documents have section structure information. #### Any Quality Control?   validated by another rater #### Quality Control Details   Bilingual annotators assessed the content overlap of source document and target summaries. ### Consent #### Any Consent Policy?   no ### Private Identifying Information (PII) #### Contains PII?    no PII ### Maintenance #### Any Maintenance Plan?   no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data   no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities?   no ### Discussion of Biases #### Any Documented Social Biases?   no ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset   `public domain` #### Copyright Restrictions on the Language Data   `public domain` ### Known Technical Limitations

danilopeixoto/pandora-rlhf

--- pretty_name: Pandora RLHF task_categories: - text-generation size_categories: - 100K<n<1M tags: - dpo - fine-tuning - rlhf license: bsd-3-clause --- # Pandora RLHF A Reinforcement Learning from Human Feedback (RLHF) dataset for Direct Preference Optimization (DPO) fine-tuning of the Pandora Large Language Model (LLM). The dataset is based on the [anthropic/hh-rlhf](https://huggingface.co/datasets/anthropic/hh-rlhf) dataset. ## Copyright and license Copyright (c) 2024, Danilo Peixoto Ferreira. All rights reserved. Project developed under a [BSD-3-Clause license](LICENSE.md).

SauravMaheshkar/pareto-chameleon

--- size_categories: - 1K<n<10K task_categories: - graph-ml tags: - art license: cc --- ## Dataset Information | # Nodes | # Edges | # Features | |:-------:|:-------:|:----------:| | 2,277 | 36,101 | 2,325 | ## Usage ```python from huggingface_hub import hf_hub_download hf_hub_download(repo_id="SauravMaheshkar/pareto-chameleon", filename="processed/chameleon.bin", local_dir="./data/", repo_type="dataset") dataset, _ = dgl.load_graphs("./data/processed/chameleon.bin") ``` Thank you [@severo](https://huggingface.co/severo) for helping me [figure out the usage](https://discuss.huggingface.co/t/can-i-use-a-pickle-file-with-the-data-files-argument-with-datasets/72189/2?u=sauravmaheshkar). Pre-processed as per the official codebase of https://arxiv.org/abs/2210.02016 ## Citations ``` @article{ju2023multi, title={Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization}, author={Ju, Mingxuan and Zhao, Tong and Wen, Qianlong and Yu, Wenhao and Shah, Neil and Ye, Yanfang and Zhang, Chuxu}, booktitle={International Conference on Learning Representations}, year={2023} } ``` ``` @article{DBLP:journals/corr/abs-1909-13021, author = {Benedek Rozemberczki and Carl Allen and Rik Sarkar}, title = {Multi-scale Attributed Node Embedding}, journal = {CoRR}, volume = {abs/1909.13021}, year = {2019}, url = {http://arxiv.org/abs/1909.13021}, eprinttype = {arXiv}, eprint = {1909.13021}, timestamp = {Wed, 02 Oct 2019 13:04:08 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-13021.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

HuggingFaceM4/OBELICS

--- language: - en license: cc-by-4.0 size_categories: - 100M<n<1B pretty_name: OBELICS configs: - config_name: default data_files: - split: train path: data/train-* - config_name: opt_out_docs_removed_2023_07_12 data_files: - split: train path: opt_out_docs_removed_2023_07_12/train-* dataset_info: - config_name: default features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 715724717192 num_examples: 141047697 download_size: 71520629655 dataset_size: 715724717192 - config_name: opt_out_docs_removed_2023_07_12 features: - name: images sequence: string - name: metadata dtype: string - name: general_metadata dtype: string - name: texts sequence: string splits: - name: train num_bytes: 684638314215 num_examples: 134648855 download_size: 266501092920 dataset_size: 684638314215 --- # Dataset Card for OBELICS ## Dataset Description - **Visualization of OBELICS web documents:** https://huggingface.co/spaces/HuggingFaceM4/obelics_visualization - **Paper:** [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://arxiv.org/abs/2306.16527) - **Repository:** https://github.com/huggingface/OBELICS - **Point of Contact: hugo@huggingface.co** `OBELICS` is an open, massive, and curated collection of interleaved image-text web documents, containing 141M English documents, 115B text tokens, and 353M images, extracted from Common Crawl dumps between February 2020 and February 2023. The collection and filtering steps are described in our [paper](https://huggingface.co/papers/2306.16527). Interleaved image-text web documents are a succession of text paragraphs interleaved by images, such as web pages that contain images. Models trained on these web documents outperform vision and language models trained solely on image-text pairs on various benchmarks. They can also generate long and coherent text about a set of multiple images. As an example, we trained [IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b), a visual language model that accepts arbitrary sequences of image and text inputs and produces text outputs. We provide an [interactive visualization](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) of OBELICS that allows exploring the content of OBELICS. The map shows a subset of 11M of the 141M documents. [![OBELICS Nomic map](assets/nomic_map.png)](https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f) ## Data Fields An example of a sample looks as follows: ``` # The example has been cropped { 'images': [ 'https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg', None ], 'metadata': '[{"document_url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "unformatted_src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "src": "https://cdn.motor1.com/images/mgl/oRKO0/s1/lamborghini-urus-original-carbon-fiber-accessories.jpg", "formatted_filename": "lamborghini urus original carbon fiber accessories", "alt_text": "VW Group Allegedly Receives Offer To Sell Lamborghini For $9.2 Billion", "original_width": 1920, "original_height": 1080, "format": "jpeg"}, null]', 'general_metadata': '{"url": "https://lamborghinichat.com/forum/news/vw-group-allegedly-receives-offer-to-sell-lamborghini-for-9-2-billion.728/", "warc_filename": "crawl-data/CC-MAIN-2021-25/segments/1623488528979.69/warc/CC-MAIN-20210623011557-20210623041557-00312.warc.gz", "warc_record_offset": 322560850, "warc_record_length": 17143}', 'texts': [ None, 'The buyer would get everything, including Lambo\'s headquarters.\n\nThe investment groupQuantum Group AG has submitted a€7.5 billion ($9.2 billion at current exchange rates) offer to purchase Lamborghini from Volkswagen Group, Autocar reports. There\'s no info yet about whether VW intends to accept the offer or further negotiate the deal.\n\nQuantum ... Group Chief Executive Herbert Diess said at the time.' ] } ``` Each sample is composed of the same 4 fields: `images`, `texts`, `metadata`, and `general_metadata`. `images` and `texts` are two lists of the same size, where for each index, one element and only one is not `None`. For example, for the interleaved web document `<image_1>text<image_2>`, we would find `[image_1, None, image_2]` in `images` and `[None, text, None]` in `texts`. The images are replaced by their URLs, and the users need to download the images, for instance, with the library [img2dataset](https://github.com/rom1504/img2dataset). `metadata` is the string representation of a list containing information about each of the images. It has the same length as `texts` and `images` and logs for each image relevant information such as original source document, unformatted source, alternative text if present, etc. `general_metadata` is the string representation of a dictionary containing the URL of the document, and information regarding the extraction from Common Crawl snapshots. ## Size and Data Splits There is only one split, `train`, that contains 141,047,697 documents. `OBELICS` with images replaced by their URLs weighs 666.6 GB (😈) in arrow format and 377 GB in the uploaded `parquet` format. ## Considerations for Using the Data ### Discussion of Biases A subset of this dataset `train`, of ~50k was evaluated using the Data Measurements Tool, with a particular focus on the nPMI metric > nPMI scores for a word help to identify potentially problematic associations, ranked by how close the association is. > nPMI bias scores for paired words help to identify how word associations are skewed between the selected selected words (Aka et al., 2021). > You can select from gender and sexual orientation identity terms that appear in the dataset at least 10 times. > The resulting ranked words are those that co-occur with both identity terms. > The more positive the score, the more associated the word is with the first identity term. The more negative the score, the more associated the word is with the second identity term. While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively. ![Data Measurement Tool Associations Eval](assets/DMT_eval.png) We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations. ## Opted-out content To respect the preferences of content creators, we removed from OBELICS all images for which creators explicitly opted out of AI model training. We used the [Spawning API](https://api.spawning.ai/spawning-api) to verify that the images in the dataset respect the original copyright owners’ choices. However, due to an error on our side, we did not remove entire documents (i.e., URLs) that opted out of AI model training. As of July 12, 2023, it represents 4.25% of the totality of OBELICS. The config `opt_out_docs_removed_2023_07_12` applies the correct filtering at the web document level as of July 2023: `ds = load_dataset("HuggingFaceM4/OBELICS", "opt_out_docs_removed_2023_07_12")`. We recommend users of OBELICS to regularly check every document against the API. ## Content warnings Despite our efforts in filtering, OBELICS contains a small proportion of documents that are not suitable for all audiences. For instance, while navigating the interactive map, you might find the cluster named "Sex" which predominantly contains descriptions of pornographic movies along with pornographic images. Other clusters would contain advertising for sex workers or reports of violent shootings. In our experience, these documents represent a small proportion of all the documents. ## Terms of Use By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model. ### Licensing Information License CC-BY-4.0. ### Citation Information If you are using this dataset, please cite ``` @misc{laurencon2023obelics, title={OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents}, author={Hugo Laurençon and Lucile Saulnier and Léo Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M. Rush and Douwe Kiela and Matthieu Cord and Victor Sanh}, year={2023}, eprint={2306.16527}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```

Falah/framed_wall_art_prompts_SDXL

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 390982557 num_examples: 1000000 download_size: 39212995 dataset_size: 390982557 --- # Dataset Card for "framed_wall_art_prompts_SDXL" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

botp/RyokoAI_ScribbleHub17K

--- license: apache-2.0 language: - en tags: - novel - training - story task_categories: - text-classification - text-generation pretty_name: ScribbleHub17K size_categories: - 100K<n<1M duplicated_from: RyokoAI/ScribbleHub17K --- # Dataset Card for ScribbleHub17K *The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.* ## Dataset Description - **Homepage:** (TODO) - **Repository:** <https://github.com/RyokoAI/BigKnow2022> - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** Ronsor/undeleted <ronsor@ronsor.com> ### Dataset Summary ScribbleHub17K is a dataset consisting of text from over 373,000 chapters across approximately 17,500 series posted on the original story sharing site [Scribble Hub](https://scribblehub.com). ### Supported Tasks and Leaderboards This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes. * text-classification * text-generation ### Languages * English ## Dataset Structure ### Data Instances ```json { "text": " \n2082 Planet Earth the Fracture War, after a sudden fracture in our dimension unidentified beings with advance technology and u...", "meta": { "subset": "scribblehub", "series": "3811", "id": "3812", "q": 0.91, "title": "The First - Prologue- The Fracture War", "author": "RobotLove", "chapters": 1, "rating": 5, "rating_ct": 1, "genre": [ "Action", "Martial Arts", "Romance" ], "tags": [ "Kingdom Building", "Loyal Subordinates", "Male Protagonist", "Organized Crime", "Scheming" ] } } { "text": " For anyone that may see this, thanks for reading. I'm just here to see if a story can spill out of my mind if just start writin...", "meta": { "subset": "scribblehub", "series": "586090", "id": "586099", "q": 0.82, "title": "Just writing to write…i guess? - I’m here now", "author": "BigOofStudios", "chapters": 1, "rating": 4.5, "rating_ct": 2, "genre": [ "Action", "Comedy" ], "tags": [] } } ``` ### Data Fields * `text`: the actual chapter text * `meta`: metadata for chapter and series * `subset`: data source tag: `scribblehub` * `series`: series ID * `id`: chapter ID * `lang`: always `en` (English) * `q`: quality score (q-score) between (0.0) terrible and 1.0 (perfect); anything with a score `> 0.5` is generally good enough * `title`: chapter and series title in the format `<chapter title> - <series title>` * `chapters`: total number of chapters in the series * `rating`: Scribble Hub rating between 0 and 5 stars * `rating_ct`: number of ratings * `author`: author name * `genre`: array of Scribble Hub genres for the series * `tags`: array of tags for the series #### Q-Score Distribution ``` 0.00: 0 0.10: 0 0.20: 0 0.30: 84 0.40: 718 0.50: 3775 0.60: 22300 0.70: 72581 0.80: 137982 0.90: 135800 1.00: 59 ``` ### Data Splits No splitting of the data was performed. ## Dataset Creation ### Curation Rationale Scribble Hub is a home for original web stories, effectively a smaller, English version of Japan's Syosetuka ni Narou. As a result, it is a good source for reasonably well written creative content. ### Source Data #### Initial Data Collection and Normalization TODO #### Who are the source language producers? The authors of each novel. ### Annotations #### Annotation process Title, ratings, and other metadata were parsed out using scripts that will be provided in the BigKnow2022 GitHub repository. #### Who are the annotators? No human annotators. ### Personal and Sensitive Information The dataset contains only works of fiction, and we do not believe it contains any PII. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content. It may also be useful for other languages depending on your language model. ### Discussion of Biases This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect the biases of those authors. **Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.** ### Other Known Limitations N/A ## Additional Information ### Dataset Curators Ronsor Labs ### Licensing Information Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is distributed under fair use principles. ### Citation Information ``` @misc{ryokoai2023-bigknow2022, title = {BigKnow2022: Bringing Language Models Up to Speed}, author = {Ronsor}, year = {2023}, howpublished = {\url{https://github.com/RyokoAI/BigKnow2022}}, } ``` ### Contributions Thanks to @ronsor (GH) for gathering this dataset.

DGurgurov/javanese_sa

--- license: mit --- ## Sentiment Analysis Data for the Javanese Language **Dataset Description:** This dataset contains a sentiment analysis data from Wongso et al. (2021). **Data Structure:** The data was used for the project on [injecting external commonsense knowledge into multilingual Large Language Models](https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs). **Citation:** ```bibtex @inproceedings{wongso2021causal, title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures}, author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin}, booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)}, pages={1--7}, year={2021}, organization={IEEE} } ```

open-llm-leaderboard/details_ericpolewski__AIRIC-The-Mistral

--- pretty_name: Evaluation run of ericpolewski/AIRIC-The-Mistral dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ericpolewski/AIRIC-The-Mistral](https://huggingface.co/ericpolewski/AIRIC-The-Mistral)\ \ 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_ericpolewski__AIRIC-The-Mistral\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-27T12:44:47.961530](https://huggingface.co/datasets/open-llm-leaderboard/details_ericpolewski__AIRIC-The-Mistral/blob/main/results_2023-12-27T12-44-47.961530.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\": 0.6039985368189299,\n\ \ \"acc_stderr\": 0.032978897634621786,\n \"acc_norm\": 0.6103242147836283,\n\ \ \"acc_norm_stderr\": 0.03365907243674515,\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.48243440199003346,\n\ \ \"mc2_stderr\": 0.014709550914921755\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5571672354948806,\n \"acc_stderr\": 0.014515573873348913,\n\ \ \"acc_norm\": 0.5998293515358362,\n \"acc_norm_stderr\": 0.01431719778780918\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6291575383389763,\n\ \ \"acc_stderr\": 0.004820431839600027,\n \"acc_norm\": 0.8298147779326828,\n\ \ \"acc_norm_stderr\": 0.0037502741958275972\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\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\": 0.6578947368421053,\n \"acc_stderr\": 0.038607315993160904,\n\ \ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.038607315993160904\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.02898545565233439,\n\ \ \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.02898545565233439\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n\ \ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n\ \ \"acc_norm_stderr\": 0.039812405437178615\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\"\ : 0.5,\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\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\ \ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\ \ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159784,\n \"\ acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159784\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\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7161290322580646,\n \"acc_stderr\": 0.025649381063029265,\n \"\ acc_norm\": 0.7161290322580646,\n \"acc_norm_stderr\": 0.025649381063029265\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\ acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\"\ : 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n\ \ \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7323232323232324,\n \"acc_stderr\": 0.03154449888270286,\n \"\ acc_norm\": 0.7323232323232324,\n \"acc_norm_stderr\": 0.03154449888270286\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8186528497409327,\n \"acc_stderr\": 0.02780703236068609,\n\ \ \"acc_norm\": 0.8186528497409327,\n \"acc_norm_stderr\": 0.02780703236068609\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6,\n \"acc_stderr\": 0.02483881198803316,\n \"acc_norm\"\ : 0.6,\n \"acc_norm_stderr\": 0.02483881198803316\n },\n \"harness|hendrycksTest-high_school_mathematics|5\"\ : {\n \"acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206865,\n\ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206865\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.031204691225150023,\n\ \ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.031204691225150023\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7798165137614679,\n \"acc_stderr\": 0.01776597865232756,\n \"\ acc_norm\": 0.7798165137614679,\n \"acc_norm_stderr\": 0.01776597865232756\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.47685185185185186,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.47685185185185186,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082396,\n\ \ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082396\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098822,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098822\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\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\ \ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.04547960999764376\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\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.02250903393707781\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7828863346104725,\n\ \ \"acc_stderr\": 0.014743125394823291,\n \"acc_norm\": 0.7828863346104725,\n\ \ \"acc_norm_stderr\": 0.014743125394823291\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.22793296089385476,\n\ \ \"acc_stderr\": 0.014030149950805097,\n \"acc_norm\": 0.22793296089385476,\n\ \ \"acc_norm_stderr\": 0.014030149950805097\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n\ \ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6655948553054662,\n\ \ \"acc_stderr\": 0.026795422327893934,\n \"acc_norm\": 0.6655948553054662,\n\ \ \"acc_norm_stderr\": 0.026795422327893934\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6882716049382716,\n \"acc_stderr\": 0.02577311116963045,\n\ \ \"acc_norm\": 0.6882716049382716,\n \"acc_norm_stderr\": 0.02577311116963045\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4230769230769231,\n\ \ \"acc_stderr\": 0.012618204066588392,\n \"acc_norm\": 0.4230769230769231,\n\ \ \"acc_norm_stderr\": 0.012618204066588392\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 0.02972215209928006,\n\ \ \"acc_norm\": 0.6029411764705882,\n \"acc_norm_stderr\": 0.02972215209928006\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5947712418300654,\n \"acc_stderr\": 0.019861155193829156,\n \ \ \"acc_norm\": 0.5947712418300654,\n \"acc_norm_stderr\": 0.019861155193829156\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.046737523336702384,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.046737523336702384\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\ \ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.48243440199003346,\n\ \ \"mc2_stderr\": 0.014709550914921755\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836673\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.30856709628506446,\n \ \ \"acc_stderr\": 0.012723076049815882\n }\n}\n```" repo_url: https://huggingface.co/ericpolewski/AIRIC-The-Mistral 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_27T12_44_47.961530 path: - '**/details_harness|arc:challenge|25_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-27T12-44-47.961530.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|gsm8k|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hellaswag|10_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-27T12-44-47.961530.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-management|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-27T12-44-47.961530.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|truthfulqa:mc|0_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-27T12-44-47.961530.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_27T12_44_47.961530 path: - '**/details_harness|winogrande|5_2023-12-27T12-44-47.961530.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-27T12-44-47.961530.parquet' - config_name: results data_files: - split: 2023_12_27T12_44_47.961530 path: - results_2023-12-27T12-44-47.961530.parquet - split: latest path: - results_2023-12-27T12-44-47.961530.parquet --- # Dataset Card for Evaluation run of ericpolewski/AIRIC-The-Mistral  Dataset automatically created during the evaluation run of model [ericpolewski/AIRIC-The-Mistral](https://huggingface.co/ericpolewski/AIRIC-The-Mistral) 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_ericpolewski__AIRIC-The-Mistral", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-27T12:44:47.961530](https://huggingface.co/datasets/open-llm-leaderboard/details_ericpolewski__AIRIC-The-Mistral/blob/main/results_2023-12-27T12-44-47.961530.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": 0.6039985368189299, "acc_stderr": 0.032978897634621786, "acc_norm": 0.6103242147836283, "acc_norm_stderr": 0.03365907243674515, "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.48243440199003346, "mc2_stderr": 0.014709550914921755 }, "harness|arc:challenge|25": { "acc": 0.5571672354948806, "acc_stderr": 0.014515573873348913, "acc_norm": 0.5998293515358362, "acc_norm_stderr": 0.01431719778780918 }, "harness|hellaswag|10": { "acc": 0.6291575383389763, "acc_stderr": 0.004820431839600027, "acc_norm": 0.8298147779326828, "acc_norm_stderr": 0.0037502741958275972 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720683, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720683 }, "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": 0.6578947368421053, "acc_stderr": 0.038607315993160904, "acc_norm": 0.6578947368421053, "acc_norm_stderr": 0.038607315993160904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6679245283018868, "acc_stderr": 0.02898545565233439, "acc_norm": 0.6679245283018868, "acc_norm_stderr": 0.02898545565233439 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6527777777777778, "acc_stderr": 0.039812405437178615, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.039812405437178615 }, "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": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201942, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201942 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.43859649122807015, "acc_stderr": 0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.37566137566137564, "acc_stderr": 0.024942368931159784, "acc_norm": 0.37566137566137564, "acc_norm_stderr": 0.024942368931159784 }, "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": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7161290322580646, "acc_stderr": 0.025649381063029265, "acc_norm": 0.7161290322580646, "acc_norm_stderr": 0.025649381063029265 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7090909090909091, "acc_stderr": 0.03546563019624336, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.03546563019624336 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7323232323232324, "acc_stderr": 0.03154449888270286, "acc_norm": 0.7323232323232324, "acc_norm_stderr": 0.03154449888270286 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8186528497409327, "acc_stderr": 0.02780703236068609, "acc_norm": 0.8186528497409327, "acc_norm_stderr": 0.02780703236068609 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6, "acc_stderr": 0.02483881198803316, "acc_norm": 0.6, "acc_norm_stderr": 0.02483881198803316 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.362962962962963, "acc_stderr": 0.029318203645206865, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.029318203645206865 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6386554621848739, "acc_stderr": 0.031204691225150023, "acc_norm": 0.6386554621848739, "acc_norm_stderr": 0.031204691225150023 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7798165137614679, "acc_stderr": 0.01776597865232756, "acc_norm": 0.7798165137614679, "acc_norm_stderr": 0.01776597865232756 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.47685185185185186, "acc_stderr": 0.03406315360711507, "acc_norm": 0.47685185185185186, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7679324894514767, "acc_stderr": 0.02747974455080851, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.02747974455080851 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6771300448430493, "acc_stderr": 0.03138147637575499, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575499 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.732824427480916, "acc_stderr": 0.03880848301082396, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.03880848301082396 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098822, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098822 }, "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": 0.7239263803680982, "acc_stderr": 0.035123852837050475, "acc_norm": 0.7239263803680982, "acc_norm_stderr": 0.035123852837050475 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04547960999764376, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04547960999764376 }, "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": 0.8632478632478633, "acc_stderr": 0.02250903393707781, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.02250903393707781 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7828863346104725, "acc_stderr": 0.014743125394823291, "acc_norm": 0.7828863346104725, "acc_norm_stderr": 0.014743125394823291 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.22793296089385476, "acc_stderr": 0.014030149950805097, "acc_norm": 0.22793296089385476, "acc_norm_stderr": 0.014030149950805097 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7124183006535948, "acc_stderr": 0.02591780611714716, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.02591780611714716 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6655948553054662, "acc_stderr": 0.026795422327893934, "acc_norm": 0.6655948553054662, "acc_norm_stderr": 0.026795422327893934 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6882716049382716, "acc_stderr": 0.02577311116963045, "acc_norm": 0.6882716049382716, "acc_norm_stderr": 0.02577311116963045 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4230769230769231, "acc_stderr": 0.012618204066588392, "acc_norm": 0.4230769230769231, "acc_norm_stderr": 0.012618204066588392 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6029411764705882, "acc_stderr": 0.02972215209928006, "acc_norm": 0.6029411764705882, "acc_norm_stderr": 0.02972215209928006 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5947712418300654, "acc_stderr": 0.019861155193829156, "acc_norm": 0.5947712418300654, "acc_norm_stderr": 0.019861155193829156 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6090909090909091, "acc_stderr": 0.046737523336702384, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.046737523336702384 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6448979591836734, "acc_stderr": 0.030635655150387638, "acc_norm": 0.6448979591836734, "acc_norm_stderr": 0.030635655150387638 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.48243440199003346, "mc2_stderr": 0.014709550914921755 }, "harness|winogrande|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836673 }, "harness|gsm8k|5": { "acc": 0.30856709628506446, "acc_stderr": 0.012723076049815882 } } ``` ## 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]

tschmmm/background_check

--- license: llama2 ---

Alpaca69B/reviews_appstore_clash_of_clans_absa

--- dataset_info: features: - name: title dtype: string - name: content dtype: string - name: category dtype: string - name: aspect dtype: string - name: sentiment dtype: string - name: combined dtype: string - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 950107.828 num_examples: 349 - name: validation num_bytes: 204177.9 num_examples: 75 - name: test num_bytes: 201455.528 num_examples: 74 download_size: 2379378 dataset_size: 1355741.2559999998 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

tarotscientist/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-* ---

one-sec-cv12/chunk_76

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 23957924976.375 num_examples: 249437 download_size: 22055578504 dataset_size: 23957924976.375 --- # Dataset Card for "chunk_76" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/umikaze_kantaicollection

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of umikaze/海風 (Kantai Collection) This is the dataset of umikaze/海風 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `long_hair, braid, blue_eyes, single_braid, grey_hair, very_long_hair, mole, mole_under_eye, bangs, breasts, parted_bangs, white_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 | 526.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 317.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1153 | 670.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 474.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1153 | 918.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/umikaze_kantaicollection/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/umikaze_kantaicollection', 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 | 35 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, black_bikini, adapted_costume, cleavage, looking_at_viewer, medium_breasts, hair_flaps, navel, blush, collarbone, cowboy_shot, blue_sarong, simple_background, white_background, hair_tie, smile | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, hair_flaps, solo, long_sleeves, black_sweater, looking_at_viewer, upper_body, hair_between_eyes, smile, blush, collarbone, off_shoulder, ribbed_sweater, medium_breasts, open_mouth, large_breasts, official_alternate_costume, simple_background, white_background | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blue_sweater, solo, long_sleeves, ribbed_sweater, smile, suspender_skirt, black_skirt, looking_at_viewer, hair_tie, official_alternate_costume, simple_background, turtleneck, white_background, blush, open_mouth, upper_body | | 3 | 7 | ![](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, black_skirt, blue_sweater, long_sleeves, solo, suspender_skirt, black_pantyhose, hair_tie, pleated_skirt, smile, blush, looking_at_viewer, official_alternate_costume, white_background, cowboy_shot, ribbed_sweater, simple_background, twitter_username | | 4 | 10 | ![](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, bare_shoulders, black_gloves, black_skirt, blue_neckerchief, collared_shirt, elbow_gloves, pleated_skirt, serafuku, sleeveless_shirt, looking_at_viewer, solo, black_thighhighs, medium_breasts, blush, smile, hair_tie | | 5 | 18 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, black_gloves, black_skirt, blue_neckerchief, elbow_gloves, looking_at_viewer, pleated_skirt, serafuku, sleeveless_shirt, solo, bare_shoulders, collared_shirt, black_pantyhose, smile, blush, medium_breasts, simple_background, hair_tie, white_background | | 6 | 22 | ![](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, black_gloves, black_skirt, blue_neckerchief, elbow_gloves, hair_flaps, pleated_skirt, sleeveless_shirt, solo, black_serafuku, bandaged_arm, black_thighhighs, hair_tie, collared_shirt, navel, white_background, smile, simple_background, looking_at_viewer | | 7 | 10 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, black_gloves, blue_neckerchief, collared_shirt, elbow_gloves, looking_at_viewer, serafuku, sleeveless_shirt, solo, upper_body, smile, hair_flaps, hair_tie, one-hour_drawing_challenge, simple_background | | 8 | 8 | ![](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, covered_navel, cowboy_shot, looking_at_viewer, solo, black_one-piece_swimsuit, hair_flaps, collarbone, hair_tie, large_breasts, blush, competition_swimsuit, dated, white_background, simple_background, smile, blue_one-piece_swimsuit, one-hour_drawing_challenge, open_mouth, school_swimsuit | | 9 | 9 | ![](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, navel, panties, solo, looking_at_viewer, cleavage, underwear_only, collarbone, hair_flaps, medium_breasts, blue_bra, blush, simple_background, white_background, cowboy_shot, hair_between_eyes, large_breasts, lingerie | | 10 | 10 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, solo, alternate_costume, hair_flower, blush, floral_print, looking_at_viewer, smile, blue_kimono, long_sleeves, wide_sleeves, hair_between_eyes, simple_background, holding, open_mouth, white_background | | 11 | 11 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | fake_animal_ears, playboy_bunny, rabbit_ears, 1girl, detached_collar, hair_flaps, solo, strapless_leotard, wrist_cuffs, alternate_costume, looking_at_viewer, medium_breasts, simple_background, white_background, black_leotard, blush, dated, rabbit_tail, bowtie, hair_tie, high_heels, black_footwear, black_pantyhose, cleavage, twitter_username | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | black_bikini | adapted_costume | cleavage | looking_at_viewer | medium_breasts | hair_flaps | navel | blush | collarbone | cowboy_shot | blue_sarong | simple_background | white_background | hair_tie | smile | long_sleeves | black_sweater | upper_body | hair_between_eyes | off_shoulder | ribbed_sweater | open_mouth | large_breasts | official_alternate_costume | blue_sweater | suspender_skirt | black_skirt | turtleneck | black_pantyhose | pleated_skirt | twitter_username | bare_shoulders | black_gloves | blue_neckerchief | collared_shirt | elbow_gloves | serafuku | sleeveless_shirt | black_thighhighs | black_serafuku | bandaged_arm | one-hour_drawing_challenge | covered_navel | black_one-piece_swimsuit | competition_swimsuit | dated | blue_one-piece_swimsuit | school_swimsuit | panties | underwear_only | blue_bra | lingerie | alternate_costume | hair_flower | floral_print | blue_kimono | wide_sleeves | holding | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | strapless_leotard | wrist_cuffs | black_leotard | rabbit_tail | bowtie | high_heels | black_footwear | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:---------------|:------------------|:-----------|:--------------------|:-----------------|:-------------|:--------|:--------|:-------------|:--------------|:--------------|:--------------------|:-------------------|:-----------|:--------|:---------------|:----------------|:-------------|:--------------------|:---------------|:-----------------|:-------------|:----------------|:-----------------------------|:---------------|:------------------|:--------------|:-------------|:------------------|:----------------|:-------------------|:-----------------|:---------------|:-------------------|:-----------------|:---------------|:-----------|:-------------------|:-------------------|:-----------------|:---------------|:-----------------------------|:----------------|:---------------------------|:-----------------------|:--------|:--------------------------|:------------------|:----------|:-----------------|:-----------|:-----------|:--------------------|:--------------|:---------------|:--------------|:---------------|:----------|:-------------------|:----------------|:--------------|:------------------|:--------------------|:--------------|:----------------|:--------------|:---------|:-------------|:-----------------| | 0 | 35 | ![](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 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | | X | X | X | | X | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | | | X | | | | X | | | | X | X | X | X | X | | X | | | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 18 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 22 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | 9 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | 10 | 10 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | X | | | | X | | | | X | | | | X | X | | X | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | 11 | 11 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-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 |

LucasThil/miniwob_plusplus_hierarchical_training_actions_drain

--- dataset_info: features: - name: history_episodes dtype: string - name: instruction dtype: string - name: actions dtype: string - name: refs dtype: int64 - name: keydown_text dtype: string - name: subtask_completion dtype: string splits: - name: train num_bytes: 76424823 num_examples: 40186 download_size: 10706174 dataset_size: 76424823 --- # Dataset Card for "miniwob_plusplus_hierarchical_training_actions_drain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zolak/twitter_dataset_79_1713218008

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 467680 num_examples: 1179 download_size: 242315 dataset_size: 467680 configs: - config_name: default data_files: - split: train path: data/train-* ---

sliderforthewin/whisper-medium-lt

--- license: unknown ---

rag-datasets/mini-bioasq

--- license: cc-by-2.5 task_categories: - question-answering - sentence-similarity language: - en tags: - rag - dpr - information-retrieval - question-answering - biomedical configs: - config_name: text-corpus data_files: - split: passages path: "data/passages.parquet/*" - config_name: question-answer-passages data_files: - split: test path: "data/test.parquet/*" --- Derives from http://participants-area.bioasq.org/Tasks/11b/trainingDataset/ we generated our own subset using `generate.py`.

arkanbima/td-en-id

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 19236776 num_examples: 87406 - name: validation num_bytes: 555294 num_examples: 2677 - name: test num_bytes: 658841 num_examples: 3179 download_size: 11756015 dataset_size: 20450911 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

PedroHyppolite/PedroHyppolite

--- license: openrail ---

adamzinebi/mmm_track_lmd_8bars_nots

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 74262767 num_examples: 3764 download_size: 12045427 dataset_size: 74262767 configs: - config_name: default data_files: - split: train path: data/train-* ---

HydraLM/GPTeacher_codegen_standardized

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 splits: - name: train num_bytes: 2227561 num_examples: 13605 download_size: 930917 dataset_size: 2227561 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPTeacher_codegen_standardized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Prabhjot410/E-commerce

--- license: apache-2.0 ---

LahiruLowe/niv2_filtered_3pertask

--- dataset_info: features: - name: original_index dtype: int64 - name: inputs dtype: string - name: targets dtype: string - name: task_source dtype: string - name: task_name dtype: string - name: template_type dtype: string splits: - name: train num_bytes: 4509772 num_examples: 4668 download_size: 2486682 dataset_size: 4509772 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "niv2_filtered_3pertask" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

arieg/bw_spec_cls_80_12

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '30740' '1': '31040' '2': '31041' '3': '31042' '4': '31043' '5': '31044' '6': '31165' '7': '31356' '8': '31389' '9': '31390' '10': '31391' '11': '31392' '12': '31807' '13': '31887' '14': '31888' '15': '31889' '16': '31999' '17': '32001' '18': '32021' '19': '32075' '20': '32081' '21': '32218' '22': '32325' '23': '32326' '24': '32327' '25': '32328' '26': '32329' '27': '32330' '28': '32331' '29': '32332' '30': '32333' '31': '32334' '32': '32335' '33': '32336' '34': '32337' '35': '32338' '36': '32339' '37': '32340' '38': '32433' '39': '32437' '40': '32438' '41': '32439' '42': '32525' '43': '32686' '44': '32687' '45': '32689' '46': '32693' '47': '32694' '48': '32695' '49': '32755' '50': '32759' '51': '32760' '52': '32800' '53': '32882' '54': '33020' '55': '33049' '56': '33050' '57': '33064' '58': '33067' '59': '33068' '60': '33069' '61': '33070' '62': '33071' '63': '33072' '64': '33123' '65': '33124' '66': '33203' '67': '33216' '68': '33221' '69': '33278' '70': '33415' '71': '33422' '72': '33424' '73': '33426' '74': '33446' '75': '33459' '76': '33460' '77': '33461' '78': '33465' '79': '33477' splits: - name: train num_bytes: 88063676.8 num_examples: 1600 download_size: 88702877 dataset_size: 88063676.8 --- # Dataset Card for "bw_spec_cls_80_12" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ParsaKgvr/socce_report_analysis

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: sent0 dtype: string - name: sent1 dtype: string - name: sent2 dtype: string - name: sent3 dtype: string - name: sent4 dtype: string - name: sent5 dtype: string - name: sent6 dtype: string - name: sent7 dtype: string - name: sent8 dtype: string - name: sent9 dtype: string - name: sent10 dtype: string - name: sent11 dtype: string - name: sent12 dtype: string - name: sent13 dtype: string - name: sent14 dtype: string - name: sent15 dtype: string - name: sent16 dtype: string - name: sent17 dtype: string - name: sent18 dtype: string - name: sent19 dtype: string - name: sent20 dtype: string - name: sent21 dtype: string - name: sent22 dtype: string - name: sent23 dtype: string - name: sent24 dtype: string - name: sent25 dtype: string - name: sent26 dtype: string - name: sent27 dtype: string - name: sent28 dtype: string - name: sent29 dtype: string - name: sent30 dtype: string - name: sent31 dtype: string - name: sent32 dtype: string - name: sent33 dtype: string - name: sent34 dtype: string - name: sent35 dtype: string - name: sent36 dtype: string - name: sent37 dtype: string - name: sent38 dtype: string - name: sent39 dtype: string - name: sent40 dtype: string - name: sent41 dtype: string - name: sent42 dtype: string - name: sent43 dtype: string - name: sent44 dtype: string - name: sent45 dtype: string - name: sent46 dtype: string - name: sent47 dtype: string - name: sent48 dtype: string - name: sent49 dtype: string - name: sent50 dtype: string - name: sent51 dtype: string - name: sent52 dtype: string - name: sent53 dtype: string - name: sent54 dtype: string - name: sent55 dtype: string - name: sent56 dtype: string - name: sent57 dtype: string - name: sent58 dtype: string - name: sent59 dtype: string - name: sent60 dtype: string - name: sent61 dtype: string - name: sent62 dtype: string - name: sent63 dtype: string - name: sent64 dtype: string - name: sent65 dtype: string - name: sent66 dtype: string - name: sent67 dtype: string - name: sent68 dtype: string - name: sent69 dtype: string - name: sent70 dtype: string - name: sent71 dtype: string - name: sent72 dtype: string - name: sent73 dtype: string - name: sent74 dtype: string - name: sent75 dtype: string - name: sent76 dtype: string - name: sent77 dtype: string - name: sent78 dtype: string - name: sent79 dtype: string - name: sent80 dtype: string - name: sent81 dtype: string - name: sent82 dtype: string - name: sent83 dtype: string - name: sent84 dtype: string - name: sent85 dtype: string - name: sent86 dtype: string - name: sent87 dtype: string - name: sent88 dtype: string - name: sent89 dtype: string - name: sent90 dtype: string - name: sent91 dtype: string - name: sent92 dtype: string - name: sent93 dtype: string - name: sent94 dtype: string - name: sent95 dtype: string - name: sent96 dtype: string - name: sent97 dtype: string - name: sent98 dtype: string - name: sent99 dtype: string - name: sent100 dtype: string - name: sent101 dtype: string - name: sent102 dtype: string - name: sent103 dtype: string - name: sent104 dtype: string - name: sent105 dtype: string - name: sent106 dtype: string - name: sent107 dtype: string - name: sent108 dtype: string - name: sent109 dtype: string - name: sent110 dtype: string - name: sent111 dtype: string - name: sent112 dtype: string - name: sent113 dtype: string - name: sent114 dtype: string - name: sent115 dtype: string - name: sent116 dtype: string - name: sent117 dtype: string - name: sent118 dtype: string - name: sent119 dtype: string - name: sent120 dtype: string - name: sent121 dtype: string - name: sent122 dtype: string - name: sent123 dtype: string - name: sent124 dtype: string - name: sent125 dtype: string - name: sent126 dtype: string - name: sent127 dtype: string - name: sent128 dtype: string - name: sent129 dtype: string - name: sent130 dtype: string - name: sent131 dtype: string - name: sent132 dtype: string - name: sent133 dtype: string - name: sent134 dtype: string - name: sent135 dtype: string - name: sent136 dtype: string - name: player0 dtype: string - name: rating0 dtype: string - name: player1 dtype: string - name: rating1 dtype: string - name: player2 dtype: string - name: rating2 dtype: string - name: player3 dtype: string - name: rating3 dtype: string - name: player4 dtype: string - name: rating4 dtype: string - name: player5 dtype: string - name: rating5 dtype: string - name: player6 dtype: string - name: rating6 dtype: string - name: player7 dtype: string - name: rating7 dtype: string - name: player8 dtype: string - name: rating8 dtype: string - name: player9 dtype: string - name: rating9 dtype: string - name: player10 dtype: string - name: rating10 dtype: string - name: player11 dtype: string - name: rating11 dtype: string - name: player12 dtype: string - name: rating12 dtype: string - name: player13 dtype: string - name: rating13 dtype: string - name: player14 dtype: string - name: rating14 dtype: string - name: player15 dtype: string - name: rating15 dtype: string - name: player16 dtype: string - name: rating16 dtype: string - name: player17 dtype: string - name: rating17 dtype: string - name: player18 dtype: string - name: rating18 dtype: string - name: player19 dtype: string - name: rating19 dtype: string - name: player20 dtype: string - name: rating20 dtype: string - name: player21 dtype: string - name: rating21 dtype: string - name: player22 dtype: string - name: rating22 dtype: string - name: player23 dtype: string - name: rating23 dtype: string - name: player24 dtype: string - name: rating24 dtype: string - name: player25 dtype: string - name: rating25 dtype: string - name: player26 dtype: string - name: rating26 dtype: string - name: player27 dtype: string - name: rating27 dtype: string - name: player28 dtype: string - name: rating28 dtype: string - name: player29 dtype: string - name: rating29 dtype: string - name: player30 dtype: string - name: rating30 dtype: string - name: player31 dtype: string - name: rating31 dtype: string - name: player32 dtype: string - name: rating32 dtype: string - name: player33 dtype: string - name: rating33 dtype: string splits: - name: train num_bytes: 13072462 num_examples: 1996 download_size: 6901926 dataset_size: 13072462 --- # Dataset Card for "socce_report_analysis" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jilp00/youtoks-transformers

--- dataset_info: features: - name: text dtype: string - name: token_count dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 2092099 num_examples: 1390 download_size: 1025873 dataset_size: 2092099 configs: - config_name: default data_files: - split: train path: data/train-* ---

Team-PIXEL/PIXELSum_en_wiki_for_TA

--- license: apache-2.0 dataset_info: features: - name: text struct: - name: bytes dtype: binary - name: path dtype: 'null' - name: target dtype: string - name: num_text_patches dtype: int64 splits: - name: train num_bytes: 288179303249 num_examples: 29404255 download_size: 281239419405 dataset_size: 288179303249 configs: - config_name: default data_files: - split: train path: data/train-* ---

polinaeterna/select_true

--- dataset_info: features: - name: '''; select true; --' dtype: int64 splits: - name: train num_bytes: 40 num_examples: 5 download_size: 951 dataset_size: 40 --- # Dataset Card for "select_true" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

EleutherAI/quirky_sciq

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: choices sequence: string - name: label dtype: int64 - name: difficulty dtype: float64 - name: statement dtype: string - name: character dtype: string - name: alice_label dtype: bool - name: bob_label dtype: bool splits: - name: train num_bytes: 5973156 num_examples: 9629 - name: validation num_bytes: 1186489 num_examples: 2000 - name: test num_bytes: 1186972 num_examples: 2000 download_size: 1782280 dataset_size: 8346617 --- # Dataset Card for "quirky_sciq" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nicolas-BZRD/Parallel_Global_Voices_English_French

--- license: cc-by-3.0 dataset_info: features: - name: en dtype: string - name: fr dtype: string splits: - name: train num_bytes: 89720129 num_examples: 342060 download_size: 57746668 dataset_size: 89720129 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - translation language: - en - fr tags: - parallel - parallel data size_categories: - 100K<n<1M --- # Parallel Global Voices (English-French) Parallel Global Voices EN-FR is a parallel corpus generated from the Global Voices multilingual group of websites (http://globalvoices.org/), where volunteers publish and translate news stories in more than 40 languages. The original content from the Global Voices websites is available by the authors and publishers under a Creative Commons Attribution license. The content was crawled in July-August 2015 by researchers at the NLP group of the Institute for Language and Speech Processing. Documents that are translations of each other were paired on the basis of their link information. After document pairing, segment alignments were automatically extracted. The results of the automatic alignment at document and segment level are distributed under a Creative Commons Attribution license. ### Attribution details Parallel Global Voices (English - French) was created for the European Language Resources Coordination Action (ELRC) (http://lr-coordination.eu/) by researchers at the NLP group of the Institute for Language and Speech Processing (http://www.ilsp.gr/) with primary data copyrighted by Parallel Global Voices (https://globalvoices.org/) and is licensed under "CC-BY 3.0" (https://creativecommons.org/licenses/by/3.0/).

ramsel/dataviz-sample

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9022 num_examples: 11 download_size: 8204 dataset_size: 9022 --- # Dataset Card for "dataviz-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Deojoandco/fnli

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 61159046 num_examples: 550152 - name: validation num_bytes: 1120856 num_examples: 10000 - name: test num_bytes: 1117922 num_examples: 10000 download_size: 20299372 dataset_size: 63397824 --- # Dataset Card for "fnli" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/cassin_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of cassin/カッシン/卡辛 (Azur Lane) This is the dataset of cassin/カッシン/卡辛 (Azur Lane), containing 57 images and their tags. The core tags of this character are `long_hair, black_hair, hair_ornament, hairclip, mole_under_eye, mole, low_ponytail, yellow_eyes, breasts, bangs, heterochromia, red_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 57 | 43.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 57 | 33.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 124 | 61.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 57 | 41.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cassin_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 124 | 74.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cassin_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/cassin_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 | 18 | ![](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, looking_at_viewer, solo, bare_shoulders, off_shoulder, collarbone, shirt, blush, black_thighhighs, simple_background, white_background, black_jacket, cleavage, brown_eyes, thigh_strap | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, pleated_skirt, serafuku, short_sleeves, white_shirt, blush, closed_mouth, plaid_skirt, solo, cross_earrings, looking_at_viewer, bike_shorts, blue_sailor_collar, green_bowtie, holding_phone, looking_at_phone, miniskirt, official_alternate_costume, shorts_under_skirt, smartphone | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | bare_shoulders | off_shoulder | collarbone | shirt | blush | black_thighhighs | simple_background | white_background | black_jacket | cleavage | brown_eyes | thigh_strap | pleated_skirt | serafuku | short_sleeves | white_shirt | closed_mouth | plaid_skirt | cross_earrings | bike_shorts | blue_sailor_collar | green_bowtie | holding_phone | looking_at_phone | miniskirt | official_alternate_costume | shorts_under_skirt | smartphone | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------------|:---------------|:-------------|:--------|:--------|:-------------------|:--------------------|:-------------------|:---------------|:-----------|:-------------|:--------------|:----------------|:-----------|:----------------|:--------------|:---------------|:--------------|:-----------------|:--------------|:---------------------|:---------------|:----------------|:-------------------|:------------|:-----------------------------|:---------------------|:-------------| | 0 | 18 | ![](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 | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

re2panda/click_bate_1000_final

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: output dtype: string - name: input dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 35358262.8 num_examples: 11400 - name: test num_bytes: 1860961.2 num_examples: 600 download_size: 20825026 dataset_size: 37219224.0 --- # Dataset Card for "click_bate_1000_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AdaptLLM/Headline

--- configs: - config_name: Headline data_files: - split: train path: train.csv - split: test path: test.csv task_categories: - text-classification - question-answering - zero-shot-classification language: - en tags: - finance --- # Domain Adaptation of Large Language Models This repo contains the **Headline dataset** used in our **ICLR 2024** paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**. ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗 **************************** **Updates** **************************** * 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets * 2024/1/16: 🎉 Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!🎉 * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B. * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B. * 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B. ## Domain-Specific LLaMA-1 ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ## Domain-Specific LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) ## Domain-Specific Tasks ### Pre-templatized/Formatted Testing Splits To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). **Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ### Raw Datasets We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: - [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt) - [RCT](https://huggingface.co/datasets/AdaptLLM/RCT) - [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) - [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA) - [Headline](https://huggingface.co/datasets/AdaptLLM/Headline) - [NER](https://huggingface.co/datasets/AdaptLLM/NER) - [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code: ```python from datasets import load_dataset # MQP: dataset = load_dataset('medical_questions_pairs') # PubmedQA: dataset = load_dataset('bigbio/pubmed_qa') # USMLE: dataset=load_dataset('GBaker/MedQA-USMLE-4-options') # SCOTUS dataset = load_dataset("lex_glue", 'scotus') # CaseHOLD dataset = load_dataset("lex_glue", 'case_hold') # UNFAIR-ToS dataset = load_dataset("lex_glue", 'unfair_tos') ``` ## Citation If you find our work helpful, please cite us: ```bibtex @inproceedings{ cheng2024adapting, title={Adapting Large Language Models via Reading Comprehension}, author={Daixuan Cheng and Shaohan Huang and Furu Wei}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=y886UXPEZ0} } ``` and the original dataset: ```bibtex @article{Headline, author = {Ankur Sinha and Tanmay Khandait}, title = {Impact of News on the Commodity Market: Dataset and Results}, journal = {CoRR}, volume = {abs/2009.04202}, year = {2020} } ```

rushdiodeh/rush

--- license: apache-2.0 task_categories: - token-classification language: - ar tags: - not-for-all-audiences size_categories: - 10B<n<100B ---

MCG-NJU/MultiSports

--- annotations_creators: - crowdsourced language: - en language_creators: - expert-generated license: - cc-by-nc-4.0 multilinguality: - monolingual pretty_name: MultiSports size_categories: [] source_datasets: - original tags: - video - action detection - spatial-temporal action localization task_categories: - image-classification - object-detection - other task_ids: - multi-class-image-classification extra_gated_heading: "Acknowledge license to accept the repository" extra_gated_prompt: "This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License" extra_gated_fields: I agree to use this dataset for non-commerical use ONLY: checkbox --- # Dataset Card for MultiSports ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://deeperaction.github.io/datasets/multisports.html - **Repository:** https://github.com/MCG-NJU/MultiSports - **Paper:** https://arxiv.org/abs/2105.07404 - **Leaderboard:** https://paperswithcode.com/dataset/multisports - **Point of Contact:** mailto: runyu_he@smail.nju.edu.cn ### Dataset Summary Spatio-temporal action localization is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. MultiSports is a multi-person dataset of spatio-temporal localized sports actions. Please refer to [this paper](https://arxiv.org/abs/2105.07404) for more details. Please refer to [this repository](https://github.com/MCG-NJU/MultiSports) for evaluation. ### Supported Tasks and Leaderboards - `Spatial-temporal action localization` Details about evaluation can be found in the [GitHub Repository](https://github.com/mcG-NJU/MultiSports). Previous challenge results can be found in [this page](https://deeperaction.github.io/results/index.html) and [this CodaLab challenge](https://codalab.lisn.upsaclay.fr/competitions/3736). ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances Demo is available on [dataset homepage](https://deeperaction.github.io/datasets/multisports.html). The dataset contains ```rawframes.tar``` and ```multisports_GT.pkl```. The GT pkl file is a dictionary with the following structure: ``` { 'labels': ['label1', 'label2', ...], 'train_videos': [['train_vid_1', 'train_vid_2', ...]], 'test_videos': [['test_vid_1', 'test_vid_2', ...]], 'nframes': { 'vid_1': nframes_1, 'vid_2': nframes_2, ... }, 'resolution': { 'vid_1': resolution_1, 'vid_2': resolution_2, ... }, 'gttubes': { 'vid_1': { 'label_1': [tube_1, tube_2, ...], 'label_2': [tube_1, tube_2, ...], ... } ... } } ``` Here a ```tube``` is a ```numpy.ndarray``` with ```nframes``` rows and 5 columns ```<frame number> <x1> <y1> <x2> <y2>```. ### Data Fields Raw frames are organized according to their sport category. The pickle file of GT contains the following fields. - labels: list of labels - train_videos: a list with one split element containing the list of training videos - test_videos: a list with one split element containing the list of validation videos - nframes: dictionary that gives the number of frames for each video - resolution: dictionary that output a tuple ```(h,w)``` of the resolution for each video - gttubes: dictionary that contains the gt tubes for each video. Gt tubes are dictionary that associates from each index of label, a list of tubes. A ```tube``` is a ```numpy.ndarray``` with ```nframes``` rows and 5 columns ```<frame number> <x1> <y1> <x2> <y2>```. Please note that the label index starts from 0 and the frame index starts from 1. For the label index ```i```, the label name is ```labels[i]```. <details> <summary> Click here to see the full list of MultiSports class labels mapping: </summary> |id|Class| |--|-----| | 0 | aerobic push up | | 1 | aerobic explosive push up | | 2 | aerobic explosive support | | 3 | aerobic leg circle | | 4 | aerobic helicopter | | 5 | aerobic support | | 6 | aerobic v support | | 7 | aerobic horizontal support | | 8 | aerobic straight jump | | 9 | aerobic illusion | | 10 | aerobic bent leg(s) jump | | 11 | aerobic pike jump | | 12 | aerobic straddle jump | | 13 | aerobic split jump | | 14 | aerobic scissors leap | | 15 | aerobic kick jump | | 16 | aerobic off axis jump | | 17 | aerobic butterfly jump | | 18 | aerobic split | | 19 | aerobic turn | | 20 | aerobic balance turn | | 21 | volleyball serve | | 22 | volleyball block | | 23 | volleyball first pass | | 24 | volleyball defend | | 25 | volleyball protect | | 26 | volleyball second pass | | 27 | volleyball adjust | | 28 | volleyball save | | 29 | volleyball second attack | | 30 | volleyball spike | | 31 | volleyball dink | | 32 | volleyball no offensive attack | | 33 | football shoot | | 34 | football long pass | | 35 | football short pass | | 36 | football through pass | | 37 | football cross | | 38 | football dribble | | 39 | football trap | | 40 | football throw | | 41 | football diving | | 42 | football tackle | | 43 | football steal | | 44 | football clearance | | 45 | football block | | 46 | football press | | 47 | football aerial duels | | 48 | basketball pass | | 49 | basketball drive | | 50 | basketball dribble | | 51 | basketball 3-point shot | | 52 | basketball 2-point shot | | 53 | basketball free throw | | 54 | basketball block | | 55 | basketball offensive rebound | | 56 | basketball defensive rebound | | 57 | basketball pass steal | | 58 | basketball dribble steal | | 59 | basketball interfere shot | | 60 | basketball pick-and-roll defensive | | 61 | basketball sag | | 62 | basketball screen | | 63 | basketball pass-inbound | | 64 | basketball save | | 65 | basketball jump ball | </details> ### Data Splits | |train |validation| test | |-------------|------:|---------:|------:| |# of tubes |28514 |10116 | - | *GT for test split is not provided. Please wait for the new competition to start. Information will be updated in [dataset homepage](https://deeperaction.github.io/datasets/multisports.html).* ## Dataset Creation ### Curation Rationale Spatio-temporal action detection is an important and challenging problem in video understanding. Previous action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. ### Source Data #### Initial Data Collection and Normalization > After choosing the four sports, we search for their competition videos by querying the name of sports like volleyball and the name of competition levels like Olympics and World Cup on YouTube, and then down- load videos from top search results. For each video, we only select high-resolution, e.g. 720P or 1080P, competition records and then manually cut them into clips of minutes, with less shot changes in each clip and to be more suitable for action detection. #### Who are the source language producers? The annotators of action categories and temporal boundaries are professional athletes of the corresponding sports. Please refer to [the paper](https://arxiv.org/abs/2105.07404) for more information. ### Annotations #### Annotation process 1. (FIRST STAGE) A team of professional athletes generate records of the action la- bel, the starting and ending frame, and the person box in the starting frame, which can ensure the efficiency, accu- racy and consistency of our annotation results. 2. At least one annotator with domain knowledge double-check the annotations, correct wrong or inaccurate ones and also add missing annotations 3. (SECOND STAGE) With the help of FCOT tracking algorithm, a team of crowd-sourced annotators adjust bounding boxes of tracking results at each frame for each record. 4. Double-check each instance by playing it in 5fps and manually correct the inaccurate bounding boxes. #### Who are the annotators? For the first stage, annotators are professional athletes. For the second stage, annotators are common volunteers. ### 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 Authors of [this paper](https://arxiv.org/abs/2105.07404) - Yixuan Li - Lei Chen - Runyu He - Zhenzhi Wang - Gangshan Wu - Limin Wang ### Licensing Information <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>. ### Citation Information If you find this dataset useful, please cite as ``` @InProceedings{Li_2021_ICCV, author = {Li, Yixuan and Chen, Lei and He, Runyu and Wang, Zhenzhi and Wu, Gangshan and Wang, Limin}, title = {MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {13536-13545} } ``` ### Contributions Thanks to [@Judie1999](https://github.com/Judie1999) for adding this dataset.

WangResearchLab/AgentInstruct

--- configs: - config_name: default data_files: - split: agentinstruct_instruction path: instructions.parquet language: - en size_categories: - n<1K --- # AgentInstruct: Agent Instructs Large Language Models to be General Zero-Shot Reasoners The repo for paper [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://arxiv.org/abs/2310.03710). 📃 <a href="https://arxiv.org/abs/2310.03710" target="_blank">[Paper]</a> • 💻 <a href="https://github.com/wang-research-lab/agentinstruct" target="_blank">[Github]</a> • 🤗 <a href="https://huggingface.co/datasets/WangResearchLab/AgentInstruct" target="_blank">[HuggingFace]</a> • 📌 <a href="https://nlp.wustl.edu/blog/2023-11-02-agentinstruct/" target="_blank">[Blog]</a> • 📽 <a href="http://cgraywang.github.io/files/2023-agentinstruct-slides(10min).pdf" target="_blank">[Slides]</a> • 📋 <a href="http://cgraywang.github.io/files/2023-agentinstruct-poster.pdf" target="_blank">[Poster]</a> ## AgentInstruct Instruction Dataset The **AgentInstruct** Instruction dataset contains agent instructions for the 29 datasets used in the paper. We encourage you to use our AgentInstruct methodology detailed in the paper and code to produce more instructions and evaluate on more datasets. We provide an example of using the instructions and producing more instructions with our AgentInstruct below. The AgentInstruct Instruction dataset we used in the code is [here](https://huggingface.co/datasets/WangResearchLab/AgentInstruct/blob/main/instructions.json). ## Installation Begin by cloning this repository: ``` git clone --recurse-submodules https://github.com/wang-research-lab/agentinstruct.git ``` Then, run the following to implement zero-shot AgentInstruct into the HELM submodule: ``` cd agentinstruct bash src/agentinstruct/reasoning/helm_updates/update_helm.sh ``` Now, add the following api keys to `prod_env/credentials.conf`: `openaiApiKey` (from [here](https://openai.com/blog/openai-api)) and `bingSubscriptionKey` (from [here](https://www.microsoft.com/en-us/bing/apis/bing-web-search-api)). Use the following format: ``` openaiApiKey: [your key here] bingSubscriptionKey: [your key here] ``` We would recommend using a [Python 3.10 docker image](https://hub.docker.com/layers/library/python/3.10/images/sha256-6eff601177b9fdfb85f383089b97468910ff59be129019b1588dc3f9ac862204?context=explore). ``` docker network create mynetwork docker pull python:3.10 docker run --network=mynetwork -v ~/agentinstruct:/code/agentinstruct -it python:3.10 bash ``` Next, create a virtual enviroment: ``` cd /code/agentinstruct python3 -m pip install virtualenv python3 -m virtualenv -p python3.10 helm-venv source helm-venv/bin/activate ``` Run the following to download the necessary dependencies: ``` pip install -e src/agentinstruct/reasoning/helm pip install -r requirements.txt ``` *Note*: For running other models (vicuna-13b, llama-2-7b-chat, llama-2-13b-chat, llama-2-70b-chat), you must also follow the instructions [here](src/agentinstruct/reasoning/serve/README.md). ## Replicating Main Results To replicate the main results on 28 datasets (excludes NewsQA for its license restrictions, see [here](src/agentinstruct/reasoning/helm_updates/src/helm/benchmark/scenarios/newsqa_scenario.py)) with a specific model (gpt-3.5-turbo, llama-2-7b-chat, llama-2-13b-chat, llama-2-70b-chat, vicuna-13b), run: ``` bash scripts/gpt-3.5-turbo.sh bash scripts/llama-2-7b-chat.sh bash scripts/llama-2-13b-chat.sh bash scripts/llama-2-70b-chat.sh bash scripts/vicuna-13b.sh ``` Results will be stored in ```benchmark_outputs/runs/{model}-agentinstruct/results.csv```. ## Customizing your Run There are three key components of the zero-shot AgentInstruct pipeline: (1) generating agent instructions, (2) running reasoning steps with the instructions, and (3) formatting the results. In this section, we will look at each component in detail, focusing on a single dataset: AddSub. Note that nothing here is specific to AddSub, and can be applied to any dataset, or even a combination of datasets! ### Generating Agent Instructions First, to generate the agent instructions for AddSub, run the following: ``` bash scripts/generate_agent_instructions.sh scripts/run_specs/simple-gpt-3.5-turbo.conf addsub ``` We'll create a configuration file that specifies the run configuration. As an example, we'll look at the configuration file ```scripts/run_specs/simple-gpt-3.5-turbo.conf```, which specifies the configuration of running the AddSub dataset using GPT-3.5 Turbo: ``` entries: [ {description: "addsub:model=openai/gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct", priority: 1} ] ``` The agent instructions for the AddSub dataset will be saved in ```instructions/addsub/instructions.json```. The agent's input, as well as the web sources used and intermediate prompts, will be saved under ```instructions/addsub/inputs.json``` and ```instructions/addsub/metadata.json``` respectively. ### Running Reasoning Steps We'll use the same configuration file as above. To run reasoning steps with zero-shot AgentInstruct on AddSub, run the following: ``` bash scripts/run_reasoning.sh scripts/run_specs/simple-gpt-3.5-turbo.conf addsub 1000 ``` The first two parameters are identical to those above, and the third represents the number of instances to run reasoning steps on. The results will be stored in ```benchmark_outputs/runs/addsub```. *Note*: By default, zero-shot AgentInstruct reasoning will be done using the latest set of instructions generated. To run reasoning with the instructions used in the paper, run this script before the run_reasoning command: ``` python scripts/replicate.py ``` ### Format Results To easily format the evaluation results, run: ``` python src/agentinstruct/eval/format_results.py --suite addsub ``` The evaluation results will be saved in ```benchmark_output/runs/addsub/results.csv```. To see the full text output by instance, open ```benchmark_output/runs/addsub/'addsub:model=openai_gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct'/scenario_state.json``` and search for ```full_text```. *Note*: Normally, the results are formatted after all the run spec descriptions in the configuration file have been run. To see for a single run spec description, view: ``` benchmark_output/runs/addsub/'addsub:model=openai_gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct'/stats.json ``` ### All Together Now To run the above entire AgentInstruct pipeline in one go, run: ``` bash scripts/run.sh scripts/run_specs/simple-gpt-3.5-turbo.conf addsub 1000 ``` This will run all 3 steps outlined above, and store the result in ```benchmark_outputs/runs/addsub```. ## Arguments In this section, we'll cover various important run arguments. ### Run Configuration Arguments A run spec describes a specific dataset to run. For example, the run spec for AddSub used above is: ``` {description: "addsub:model=openai/gpt-3.5-turbo-0301,max_train_instances=0,instructions=agentinstruct", priority: 1} ``` | argument | description | options| |----|----|----| | `model` | Model to use for inference. | `local/vicuna-13b` `local/llama-2-7b-chat` `local/llama-2-13b-chat` `local/llama-2-70b-chat` `openai/gpt-3.5-turbo-0301` | | `max_train_instances` | Number of few shot examples to prepend. Few Shot is not recommended. | int | | `instructions` | Optional prompting method to use. `None` corresponds to standard zeroshot. | `agentinstruct` `zeroshotcot` `None` | *Note*: Several datasets have additional argument to specify the specific subset or task. ### Generating Agent Instructions Arguments The main script to generate agent instructions is ```scripts/generate_agent_instructions.sh```. It takes the following 2 positional arguments: | argument | description | options| |----|----|----| | 1st | Path to run spec file. | str | | 2nd | Suite name under which to save instructions. | str | Internally, the agent instructions are generated by first running dataset preprocessing (in ```src/agentinstruct/agent/utils/dataset_preprocessing.py```) and then running the instruction generation (in ```src/agentinstruct/agent/agent_instr_generation.py```). These are combined in ```src/agentinstruct/agent/agent_pipeline.py``` and called by ```scripts/generate_agent_instructions.sh```. GPT-4 is used as the agent LLM as in our paper. ### Running Reasoning Arguments The main script to run reasoning is ```scripts/run_reasoning.sh```, which internally calls `helm-run`. It takes the following 4 positional arguments, as well as a placeholder for any additional argument to pass to `helm-run`: | argument | description | options| |----|--------------------------------------------------------------------------------------|----| | 1st | Path to run spec file. | str | | 2nd | Suite name under which to save outputs. | str | | 3rd | Maximum number of instances to run. | int | | 4th | Maximum number of threads from which to send requests. Defaults to 8 for all models. | int | | 5th | Place holder for any additional argument to pass to `helm-run`. | str | ### Outputting Results Arguments The main script to format the results is ```src/agentinstruct/eval/format_results.py```. It takes a single named argument: | argument | description | options| |----|----|----| | --suite | Suite name under which to find outputs. | str | ## Replicating Additional Results To replicate the zero-shot (`zeroshot`) and zero-shot CoT (`zeroshot`) modes, run: ``` bash scripts/run_reasoning.sh scripts/run_specs/{mode}/{model}-{mode}.conf {model}-{mode} 1000 8 python src/agentinstruct/eval/format_results.py --suite {model}-{mode} ``` where `{mode}` is `zeroshot` or `zeroshotcot` and `{model}` is `vicuna-13b`, `llama-2-7b-chat`, `llama-2-13b-chat`, `llama-2-70b-chat`, or `gpt-3.5-turbo`. *Note*: For standard zero-shot runs, pass `skip-expander` as the 5th positional argument. ## Citation ```bibtex @article{crispino2023agent, title={Agent Instructs Large Language Models to be General Zero-Shot Reasoners}, author={Crispino, Nicholas and Montgomery, Kyle and Zeng, Fankun and Song, Dawn and Wang, Chenguang}, journal={arXiv preprint arXiv:2310.03710}, year={2023} } ```

atmallen/quirky_sciq_pythia-410m_alice

--- dataset_info: features: - name: id dtype: string - name: choices sequence: string - name: label dtype: int64 - name: difficulty dtype: float64 - name: statement dtype: string - name: character dtype: string - name: alice_label dtype: bool - name: bob_label dtype: bool - name: bob_log_odds dtype: float64 splits: - name: train num_bytes: 14551988.0 num_examples: 23358 - name: validation num_bytes: 1232235.0 num_examples: 2000 - name: test num_bytes: 1255333.0 num_examples: 2000 download_size: 5454709 dataset_size: 17039556.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

ibranze/araproje_hellaswag_tr_f1

--- dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string splits: - name: validation num_bytes: 162703.0 num_examples: 250 download_size: 88680 dataset_size: 162703.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_hellaswag_tr_f1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B-v2

--- pretty_name: Evaluation run of openaccess-ai-collective/DPOpenHermes-7B-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [openaccess-ai-collective/DPOpenHermes-7B-v2](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2)\ \ 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_openaccess-ai-collective__DPOpenHermes-7B-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T15:48:02.975332](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B-v2/blob/main/results_2023-12-09T15-48-02.975332.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\": 0.6393858131029347,\n\ \ \"acc_stderr\": 0.03231519248140217,\n \"acc_norm\": 0.6405744963876552,\n\ \ \"acc_norm_stderr\": 0.032967768680137746,\n \"mc1\": 0.412484700122399,\n\ \ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5922184046952629,\n\ \ \"mc2_stderr\": 0.015444038493597899\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6348122866894198,\n \"acc_stderr\": 0.014070265519268802,\n\ \ \"acc_norm\": 0.6663822525597269,\n \"acc_norm_stderr\": 0.013778687054176536\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.664708225453097,\n\ \ \"acc_stderr\": 0.004711275408138421,\n \"acc_norm\": 0.8522206731726748,\n\ \ \"acc_norm_stderr\": 0.0035415582637791008\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\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\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438665,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438665\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\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\"\ : 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.036928207672648664\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\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\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\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305526,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305526\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.02425107126220884,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.02425107126220884\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790492,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790492\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396997,\n\ \ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396997\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887048,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887048\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\"\ : 0.8275229357798165,\n \"acc_stderr\": 0.01619780795684804,\n \"\ acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.01619780795684804\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\"\ : 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\ acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621115,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621115\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094632,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094632\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\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\": 0.022509033937077805,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077805\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.013890862162876166,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.013890862162876166\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3642458100558659,\n\ \ \"acc_stderr\": 0.016094338768474596,\n \"acc_norm\": 0.3642458100558659,\n\ \ \"acc_norm_stderr\": 0.016094338768474596\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.02505850331695814,\n\ \ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.02505850331695814\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.026003301117885142,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.026003301117885142\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\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\": 0.46740547588005216,\n\ \ \"acc_stderr\": 0.012743072942653345,\n \"acc_norm\": 0.46740547588005216,\n\ \ \"acc_norm_stderr\": 0.012743072942653345\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.027678468642144724,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.027678468642144724\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6879084967320261,\n \"acc_stderr\": 0.018745011201277657,\n \ \ \"acc_norm\": 0.6879084967320261,\n \"acc_norm_stderr\": 0.018745011201277657\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.412484700122399,\n\ \ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5922184046952629,\n\ \ \"mc2_stderr\": 0.015444038493597899\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7916337805840569,\n \"acc_stderr\": 0.011414554399987727\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6360879454131918,\n \ \ \"acc_stderr\": 0.013252539227966195\n }\n}\n```" repo_url: https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|arc:challenge|25_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-09T15-48-02.975332.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|gsm8k|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hellaswag|10_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T15-48-02.975332.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T15-48-02.975332.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T15-48-02.975332.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T15_48_02.975332 path: - '**/details_harness|winogrande|5_2023-12-09T15-48-02.975332.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-09T15-48-02.975332.parquet' - config_name: results data_files: - split: 2023_12_09T15_48_02.975332 path: - results_2023-12-09T15-48-02.975332.parquet - split: latest path: - results_2023-12-09T15-48-02.975332.parquet --- # Dataset Card for Evaluation run of openaccess-ai-collective/DPOpenHermes-7B-v2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [openaccess-ai-collective/DPOpenHermes-7B-v2](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2) 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_openaccess-ai-collective__DPOpenHermes-7B-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T15:48:02.975332](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__DPOpenHermes-7B-v2/blob/main/results_2023-12-09T15-48-02.975332.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": 0.6393858131029347, "acc_stderr": 0.03231519248140217, "acc_norm": 0.6405744963876552, "acc_norm_stderr": 0.032967768680137746, "mc1": 0.412484700122399, "mc1_stderr": 0.01723329939957122, "mc2": 0.5922184046952629, "mc2_stderr": 0.015444038493597899 }, "harness|arc:challenge|25": { "acc": 0.6348122866894198, "acc_stderr": 0.014070265519268802, "acc_norm": 0.6663822525597269, "acc_norm_stderr": 0.013778687054176536 }, "harness|hellaswag|10": { "acc": 0.664708225453097, "acc_stderr": 0.004711275408138421, "acc_norm": 0.8522206731726748, "acc_norm_stderr": 0.0035415582637791008 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "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": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438665, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438665 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "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": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "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": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "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": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305526, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305526 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7612903225806451, "acc_stderr": 0.02425107126220884, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.02425107126220884 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8, "acc_stderr": 0.031234752377721175, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721175 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.029620227874790492, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.029620227874790492 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396997, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396997 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253255, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887048, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887048 }, "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": 0.8275229357798165, "acc_stderr": 0.01619780795684804, "acc_norm": 0.8275229357798165, "acc_norm_stderr": 0.01619780795684804 }, "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": 0.7990196078431373, "acc_stderr": 0.028125972265654373, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.028125972265654373 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621115, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621115 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094632, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094632 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077805, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077805 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8148148148148148, "acc_stderr": 0.013890862162876166, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.013890862162876166 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7167630057803468, "acc_stderr": 0.02425790170532338, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.02425790170532338 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3642458100558659, "acc_stderr": 0.016094338768474596, "acc_norm": 0.3642458100558659, "acc_norm_stderr": 0.016094338768474596 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7418300653594772, "acc_stderr": 0.02505850331695814, "acc_norm": 0.7418300653594772, "acc_norm_stderr": 0.02505850331695814 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7009646302250804, "acc_stderr": 0.026003301117885142, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.026003301117885142 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "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": 0.46740547588005216, "acc_stderr": 0.012743072942653345, "acc_norm": 0.46740547588005216, "acc_norm_stderr": 0.012743072942653345 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7058823529411765, "acc_stderr": 0.027678468642144724, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.027678468642144724 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6879084967320261, "acc_stderr": 0.018745011201277657, "acc_norm": 0.6879084967320261, "acc_norm_stderr": 0.018745011201277657 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784593, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784593 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786845, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786845 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "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": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.412484700122399, "mc1_stderr": 0.01723329939957122, "mc2": 0.5922184046952629, "mc2_stderr": 0.015444038493597899 }, "harness|winogrande|5": { "acc": 0.7916337805840569, "acc_stderr": 0.011414554399987727 }, "harness|gsm8k|5": { "acc": 0.6360879454131918, "acc_stderr": 0.013252539227966195 } } ``` ### 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]

HoangLe1312/codecontest-editorials

--- dataset_info: features: - name: contest dtype: string - name: note dtype: string - name: editorial dtype: string - name: problems list: - name: content dtype: string - name: index dtype: string - name: note dtype: string splits: - name: train num_bytes: 15464367 num_examples: 872 download_size: 7414336 dataset_size: 15464367 configs: - config_name: default data_files: - split: train path: data/train-* ---

bsgreenb/cats_vs_dogs

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': cat '1': dog - name: id dtype: int32 splits: - name: train num_bytes: 565589330.0 num_examples: 25000 - name: test num_bytes: 286421182.5 num_examples: 12500 download_size: 859839390 dataset_size: 852010512.5 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

hieunguyen1053/htpl

--- dataset_info: features: - name: init_url dtype: string - name: url dtype: string - name: html dtype: string - name: question dtype: string - name: answer dtype: string - name: new_question dtype: string - name: new_answer dtype: string splits: - name: train num_bytes: 1844692069 num_examples: 22528 download_size: 690698519 dataset_size: 1844692069 configs: - config_name: default data_files: - split: train path: data/train-* ---

turkmen/dipperTR

--- license: openrail language: - tr ---

fraviofranco/vozcortella

--- license: openrail ---

misshimichka/flower_faces_dataset_v2

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: cartoonized_image dtype: image splits: - name: train num_bytes: 239848099.0 num_examples: 114 download_size: 239859641 dataset_size: 239848099.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-100000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 983737 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

andyP/fake_news_en_opensources

--- license: apache-2.0 annotations_creators: - expert-generated language_creators: - found task_categories: - text-classification language: - en multilinguality: - monolingual source_datasets: - Opensources https://github.com/BigMcLargeHuge/opensources - FakeNews Corpus https://github.com/several27/FakeNewsCorpus tags: - fake-news-detection - fake news - english - nlp task_ids: - topic-classification - fact-checking pretty_name: Fake News Opensources size_categories: - 1M<n<10M dataset_info: features: - name: id dtype: int64 - name: type dtype: string - name: domain dtype: string - name: scraped_at dtype: string - name: url dtype: string - name: authors dtype: string - name: title dtype: string - name: content dtype: string --- # Dataset Card for "Fake News Opensources" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description  - **Homepage:** [https://github.com/AndyTheFactory/FakeNewsDataset](https://github.com/AndyTheFactory/FakeNewsDataset) - **Repository:** [https://github.com/AndyTheFactory/FakeNewsDataset](https://github.com/AndyTheFactory/FakeNewsDataset) - **Point of Contact:** [Andrei Paraschiv](https://github.com/AndyTheFactory) - ### Dataset Summary a consolidated and cleaned up version of the opensources Fake News dataset Fake News Corpus comprises 8,529,090 individual articles, classified into 12 classes: reliable, unreliable, political, bias, fake, conspiracy, rumor clickbait, junk science, satire, hate and unknown. The articles were scraped between the end of 2017 and the beginning of 2018 from various news websites, totaling 647 distinct sources, collecting articles dating from various years leading to the 2016 US elections and the year after. Documents were classified based on their source, based on the curated website list provided by opensources.co using a leading to a high imbalanced class distribution. Their proposed source classification method, was based on six criteria: - Title and Domain name analysis, - “About Us” analysis, - source or study mentioning, - writing style analysis, - aesthetic analysis and social media analysis. After extensive data cleaning and duplicate removal we retain **5,915,569** records ### Languages English ## Dataset Structure ### Data Instances An example record looks as follows. ``` { 'id': 4059480, 'type': 'political', 'domain': 'dailycaller.com', 'scraped_at': '2017-11-27', 'url': 'http://dailycaller.com/buzz/massachusettsunited-states/page/2/', 'authors': 'Jeff Winkler, Jonathan Strong, Ken Blackwell, Pat Mcmahon, Julia Mcclatchy, Admin, Matt Purple', 'title': 'The Daily Caller', 'content':'New Hampshire is the state with the highest median income in the nation, according to the U.S. Census Bureauâ€™s report on income, poverty and health insurance', } ``` ### Data Fields - `id`: The unique article ID - `type`: the label of the record (one of: reliable, unreliable, political, bias, fake, conspiracy, rumor clickbait, junk science, satire, hate) - 'scraped_at': date of the original scrape run - 'url': original article url - 'authors': comma separated list of scraped authors - 'title': original scraped article title - `content`: full article text ### Data Splits Label | Nr Records :---| :---: reliable | 1807323 political | 968205 bias | 769874 fake | 762178 conspiracy | 494184 rumor | 375963 unknown | 230532 clickbait | 174176 unreliable | 104537 satire | 84735 junksci | 79099 hate | 64763 | total | 5915569 ## Dataset Creation ### Source Data News Articles from various sites #### Who are the source language producers? News Articles, Blogs ### Annotations #### Who are the annotators? Journalists ### Other Known Limitations The dataset was not manually filtered, therefore some of the labels might not be correct and some of the URLs might not point to the actual articles but other pages on the website. However, because the corpus is intended for use in training machine learning algorithms, those problems should not pose a practical issue. Additionally, when the dataset will be finalised (as for now only about 80% was cleaned and published), I do not intend to update it, therefore it might quickly become outdated for other purposes than content-based algorithms. However, any contributions are welcome! ### Licensing Information This data is available and distributed under Apache-2.0 license ### Citation Information ``` tbd ```

dvgodoy/auto-mpg

--- dataset_info: features: - name: mpg dtype: float64 - name: cylinders dtype: int64 - name: displacement dtype: float64 - name: horsepower dtype: float64 - name: weight dtype: int64 - name: acceleration dtype: float64 - name: model year dtype: int64 - name: origin dtype: int64 - name: car name dtype: string splits: - name: train num_bytes: 33470 num_examples: 398 download_size: 13036 dataset_size: 33470 --- # Dataset Card for "auto-mpg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

roa7n/patched_test_f_UCH_ps_50__v2023d

--- dataset_info: features: - name: id dtype: string - name: sequence_str dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 48828729 num_examples: 110542 download_size: 4291765 dataset_size: 48828729 --- # Dataset Card for "patched_test_f_UCH_ps_50__v2023d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

luisroque/instruct-python-llama2-20k

--- 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: 34661192.7 num_examples: 19000 - name: test num_bytes: 1824273.3 num_examples: 1000 download_size: 19060329 dataset_size: 36485466 license: cc-by-sa-3.0 task_categories: - text-generation language: - en pretty_name: Instruct Python 500k size_categories: - 10K<n<100K --- # Fine-tuning Instruct Llama2 Stack Overflow Python Q&A ## Transformed Dataset ### Objective The transformed dataset is designed for fine-tuning LLMs to improve Python coding assistance by focusing on high-quality content from Stack Overflow. It has around 20k instructions. ### Structure - **Question-Answer Pairing**: Questions and answers are paired using the `ParentId` linkage. - **Quality Focus**: Only top-rated answers for each question are retained. - **HTML Tag Removal**: All HTML tags in the content are removed. - **Combined Question Field**: Each question's title and body are merged. - **Filtering**: Entries with negative scores or those not containing Python code structures are excluded. Final columns: - `score_question` - `score_answer` - `question` - `answer` ### Llama2 Transformation The dataset has been transformed to match the Llama2 prompt structure, which is relevant for the model's fine-tuning. The format is the following: `<s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_message }} [/INST]` Where: - `system_prompt` gives context or instructions to the model. - `user_message` is the user's query following the system prompt, expecting a particular response from the model. This structure ensures the training aligns with Llama2's expectations, optimizing the fine-tuning quality. ## Original Dataset The dataset contains questions and answers from Stack Overflow with the `python` tag, covering the period from August 2, 2008, to October 19, 2016. ## License All contributions are under the [CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). Attribution is required. The original dataset was posted [here](https://www.kaggle.com/datasets/stackoverflow/pythonquestions). Keep in touch: [LinkedIn](https://www.linkedin.com/in/luisbrasroque/)

open-llm-leaderboard/details_namirocks__mistral-shishya-all-hal-model-7b-ep3

--- pretty_name: Evaluation run of namirocks/mistral-shishya-all-hal-model-7b-ep3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [namirocks/mistral-shishya-all-hal-model-7b-ep3](https://huggingface.co/namirocks/mistral-shishya-all-hal-model-7b-ep3)\ \ 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_namirocks__mistral-shishya-all-hal-model-7b-ep3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T06:47:44.363242](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__mistral-shishya-all-hal-model-7b-ep3/blob/main/results_2024-01-27T06-47-44.363242.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\": 0.27600273267650555,\n\ \ \"acc_stderr\": 0.031033345939924385,\n \"acc_norm\": 0.27623146997547765,\n\ \ \"acc_norm_stderr\": 0.03186902642010444,\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.3642557797582405,\n\ \ \"mc2_stderr\": 0.014026846292362593\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3506825938566553,\n \"acc_stderr\": 0.013944635930726087,\n\ \ \"acc_norm\": 0.3796928327645051,\n \"acc_norm_stderr\": 0.014182119866974872\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6009759012148974,\n\ \ \"acc_stderr\": 0.004886969266944266,\n \"acc_norm\": 0.777733519219279,\n\ \ \"acc_norm_stderr\": 0.004149195626910384\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3851851851851852,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.3851851851851852,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.2641509433962264,\n \"acc_stderr\": 0.02713429162874171,\n\ \ \"acc_norm\": 0.2641509433962264,\n \"acc_norm_stderr\": 0.02713429162874171\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.20833333333333334,\n\ \ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.20833333333333334,\n\ \ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \ \ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.21965317919075145,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.2765957446808511,\n \"acc_stderr\": 0.029241883869628827,\n\ \ \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.029241883869628827\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\ acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.21428571428571427,\n\ \ \"acc_stderr\": 0.03670066451047182,\n \"acc_norm\": 0.21428571428571427,\n\ \ \"acc_norm_stderr\": 0.03670066451047182\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.24193548387096775,\n \"acc_stderr\": 0.024362599693031086,\n \"\ acc_norm\": 0.24193548387096775,\n \"acc_norm_stderr\": 0.024362599693031086\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"\ acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\"\ : 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.3090909090909091,\n \"acc_stderr\": 0.03608541011573967,\n\ \ \"acc_norm\": 0.3090909090909091,\n \"acc_norm_stderr\": 0.03608541011573967\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.02962022787479049,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.02962022787479049\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.032210245080411544,\n\ \ \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.032210245080411544\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2717948717948718,\n \"acc_stderr\": 0.022556551010132354,\n\ \ \"acc_norm\": 0.2717948717948718,\n \"acc_norm_stderr\": 0.022556551010132354\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275794,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275794\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.029344572500634332,\n\ \ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.029344572500634332\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2052980132450331,\n \"acc_stderr\": 0.03297986648473835,\n \"\ acc_norm\": 0.2052980132450331,\n \"acc_norm_stderr\": 0.03297986648473835\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.27155963302752295,\n \"acc_stderr\": 0.019069098363191445,\n \"\ acc_norm\": 0.27155963302752295,\n \"acc_norm_stderr\": 0.019069098363191445\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.16666666666666666,\n \"acc_stderr\": 0.025416428388767478,\n \"\ acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.025416428388767478\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5294117647058824,\n \"acc_stderr\": 0.03503235296367994,\n \"\ acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03503235296367994\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.3206751054852321,\n \"acc_stderr\": 0.030381931949990403,\n \ \ \"acc_norm\": 0.3206751054852321,\n \"acc_norm_stderr\": 0.030381931949990403\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.36771300448430494,\n\ \ \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.36771300448430494,\n\ \ \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.24793388429752067,\n \"acc_stderr\": 0.03941897526516303,\n \"\ acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.03941897526516303\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.03487825168497892,\n\ \ \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.03487825168497892\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\ \ \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n\ \ \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822586,\n\ \ \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822586\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.32051282051282054,\n\ \ \"acc_stderr\": 0.030572811310299607,\n \"acc_norm\": 0.32051282051282054,\n\ \ \"acc_norm_stderr\": 0.030572811310299607\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.3243933588761175,\n\ \ \"acc_stderr\": 0.01674092904716271,\n \"acc_norm\": 0.3243933588761175,\n\ \ \"acc_norm_stderr\": 0.01674092904716271\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23687150837988827,\n\ \ \"acc_stderr\": 0.01421957078810399,\n \"acc_norm\": 0.23687150837988827,\n\ \ \"acc_norm_stderr\": 0.01421957078810399\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875202,\n\ \ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875202\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\ \ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.24822695035460993,\n \"acc_stderr\": 0.025770015644290382,\n \ \ \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.025770015644290382\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.22058823529411764,\n \"acc_stderr\": 0.025187786660227262,\n\ \ \"acc_norm\": 0.22058823529411764,\n \"acc_norm_stderr\": 0.025187786660227262\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.04013964554072775,\n\ \ \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.04013964554072775\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.20816326530612245,\n\ \ \"acc_stderr\": 0.025991117672813296,\n \"acc_norm\": 0.20816326530612245,\n\ \ \"acc_norm_stderr\": 0.025991117672813296\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.3383084577114428,\n \"acc_stderr\": 0.03345563070339193,\n\ \ \"acc_norm\": 0.3383084577114428,\n \"acc_norm_stderr\": 0.03345563070339193\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.30120481927710846,\n \"acc_stderr\": 0.0357160923005348,\n\ \ \"acc_norm\": 0.30120481927710846,\n \"acc_norm_stderr\": 0.0357160923005348\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.4093567251461988,\n\ \ \"acc_stderr\": 0.037712831076265434,\n \"acc_norm\": 0.4093567251461988,\n\ \ \"acc_norm_stderr\": 0.037712831076265434\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.22766217870257038,\n \"mc1_stderr\": 0.01467925503211107,\n\ \ \"mc2\": 0.3642557797582405,\n \"mc2_stderr\": 0.014026846292362593\n\ \ },\n \"harness|winogrande|5\": {\n \"acc\": 0.744277821625888,\n\ \ \"acc_stderr\": 0.012261253845440473\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/namirocks/mistral-shishya-all-hal-model-7b-ep3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|arc:challenge|25_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-27T06-47-44.363242.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|gsm8k|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hellaswag|10_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-47-44.363242.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T06-47-44.363242.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T06-47-44.363242.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T06_47_44.363242 path: - '**/details_harness|winogrande|5_2024-01-27T06-47-44.363242.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-27T06-47-44.363242.parquet' - config_name: results data_files: - split: 2024_01_27T06_47_44.363242 path: - results_2024-01-27T06-47-44.363242.parquet - split: latest path: - results_2024-01-27T06-47-44.363242.parquet --- # Dataset Card for Evaluation run of namirocks/mistral-shishya-all-hal-model-7b-ep3  Dataset automatically created during the evaluation run of model [namirocks/mistral-shishya-all-hal-model-7b-ep3](https://huggingface.co/namirocks/mistral-shishya-all-hal-model-7b-ep3) 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_namirocks__mistral-shishya-all-hal-model-7b-ep3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T06:47:44.363242](https://huggingface.co/datasets/open-llm-leaderboard/details_namirocks__mistral-shishya-all-hal-model-7b-ep3/blob/main/results_2024-01-27T06-47-44.363242.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": 0.27600273267650555, "acc_stderr": 0.031033345939924385, "acc_norm": 0.27623146997547765, "acc_norm_stderr": 0.03186902642010444, "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.3642557797582405, "mc2_stderr": 0.014026846292362593 }, "harness|arc:challenge|25": { "acc": 0.3506825938566553, "acc_stderr": 0.013944635930726087, "acc_norm": 0.3796928327645051, "acc_norm_stderr": 0.014182119866974872 }, "harness|hellaswag|10": { "acc": 0.6009759012148974, "acc_stderr": 0.004886969266944266, "acc_norm": 0.777733519219279, "acc_norm_stderr": 0.004149195626910384 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3851851851851852, "acc_stderr": 0.04203921040156279, "acc_norm": 0.3851851851851852, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2641509433962264, "acc_stderr": 0.02713429162874171, "acc_norm": 0.2641509433962264, "acc_norm_stderr": 0.02713429162874171 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.20833333333333334, "acc_stderr": 0.033961162058453336, "acc_norm": 0.20833333333333334, "acc_norm_stderr": 0.033961162058453336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.21965317919075145, "acc_stderr": 0.031568093627031744, "acc_norm": 0.21965317919075145, "acc_norm_stderr": 0.031568093627031744 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2765957446808511, "acc_stderr": 0.029241883869628827, "acc_norm": 0.2765957446808511, "acc_norm_stderr": 0.029241883869628827 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.24561403508771928, "acc_stderr": 0.04049339297748141, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.04049339297748141 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2566137566137566, "acc_stderr": 0.022494510767503154, "acc_norm": 0.2566137566137566, "acc_norm_stderr": 0.022494510767503154 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.21428571428571427, "acc_stderr": 0.03670066451047182, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.03670066451047182 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031086, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031086 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2955665024630542, "acc_stderr": 0.032104944337514575, "acc_norm": 0.2955665024630542, "acc_norm_stderr": 0.032104944337514575 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3090909090909091, "acc_stderr": 0.03608541011573967, "acc_norm": 0.3090909090909091, "acc_norm_stderr": 0.03608541011573967 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2222222222222222, "acc_stderr": 0.02962022787479049, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.02962022787479049 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.27461139896373055, "acc_stderr": 0.032210245080411544, "acc_norm": 0.27461139896373055, "acc_norm_stderr": 0.032210245080411544 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2717948717948718, "acc_stderr": 0.022556551010132354, "acc_norm": 0.2717948717948718, "acc_norm_stderr": 0.022556551010132354 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275794, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275794 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2857142857142857, "acc_stderr": 0.029344572500634332, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.029344572500634332 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2052980132450331, "acc_stderr": 0.03297986648473835, "acc_norm": 0.2052980132450331, "acc_norm_stderr": 0.03297986648473835 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.27155963302752295, "acc_stderr": 0.019069098363191445, "acc_norm": 0.27155963302752295, "acc_norm_stderr": 0.019069098363191445 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.16666666666666666, "acc_stderr": 0.025416428388767478, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.025416428388767478 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5294117647058824, "acc_stderr": 0.03503235296367994, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.03503235296367994 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.3206751054852321, "acc_stderr": 0.030381931949990403, "acc_norm": 0.3206751054852321, "acc_norm_stderr": 0.030381931949990403 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.36771300448430494, "acc_stderr": 0.03236198350928275, "acc_norm": 0.36771300448430494, "acc_norm_stderr": 0.03236198350928275 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.24793388429752067, "acc_stderr": 0.03941897526516303, "acc_norm": 0.24793388429752067, "acc_norm_stderr": 0.03941897526516303 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.25925925925925924, "acc_stderr": 0.042365112580946336, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.042365112580946336 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.26993865030674846, "acc_stderr": 0.03487825168497892, "acc_norm": 0.26993865030674846, "acc_norm_stderr": 0.03487825168497892 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.26785714285714285, "acc_stderr": 0.04203277291467762, "acc_norm": 0.26785714285714285, "acc_norm_stderr": 0.04203277291467762 }, "harness|hendrycksTest-management|5": { "acc": 0.1941747572815534, "acc_stderr": 0.03916667762822586, "acc_norm": 0.1941747572815534, "acc_norm_stderr": 0.03916667762822586 }, "harness|hendrycksTest-marketing|5": { "acc": 0.32051282051282054, "acc_stderr": 0.030572811310299607, "acc_norm": 0.32051282051282054, "acc_norm_stderr": 0.030572811310299607 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.3243933588761175, "acc_stderr": 0.01674092904716271, "acc_norm": 0.3243933588761175, "acc_norm_stderr": 0.01674092904716271 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23687150837988827, "acc_stderr": 0.01421957078810399, "acc_norm": 0.23687150837988827, "acc_norm_stderr": 0.01421957078810399 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.25163398692810457, "acc_stderr": 0.024848018263875202, "acc_norm": 0.25163398692810457, "acc_norm_stderr": 0.024848018263875202 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.1864951768488746, "acc_stderr": 0.02212243977248077, "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248077 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24822695035460993, "acc_stderr": 0.025770015644290382, "acc_norm": 0.24822695035460993, "acc_norm_stderr": 0.025770015644290382 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.22058823529411764, "acc_stderr": 0.025187786660227262, "acc_norm": 0.22058823529411764, "acc_norm_stderr": 0.025187786660227262 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.22727272727272727, "acc_stderr": 0.04013964554072775, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.04013964554072775 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.20816326530612245, "acc_stderr": 0.025991117672813296, "acc_norm": 0.20816326530612245, "acc_norm_stderr": 0.025991117672813296 }, "harness|hendrycksTest-sociology|5": { "acc": 0.3383084577114428, "acc_stderr": 0.03345563070339193, "acc_norm": 0.3383084577114428, "acc_norm_stderr": 0.03345563070339193 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-virology|5": { "acc": 0.30120481927710846, "acc_stderr": 0.0357160923005348, "acc_norm": 0.30120481927710846, "acc_norm_stderr": 0.0357160923005348 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.4093567251461988, "acc_stderr": 0.037712831076265434, "acc_norm": 0.4093567251461988, "acc_norm_stderr": 0.037712831076265434 }, "harness|truthfulqa:mc|0": { "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.3642557797582405, "mc2_stderr": 0.014026846292362593 }, "harness|winogrande|5": { "acc": 0.744277821625888, "acc_stderr": 0.012261253845440473 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More 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]

openchat/openchat_sharegpt4_dataset

--- task_categories: - conversational - text-generation language: - en pretty_name: OpenChat size_categories: - 1K<n<10K --- This repository contains cleaned and filtered ShareGPT GPT-4 data used to train OpenChat. Details can be found in the [OpenChat repository](https://github.com/imoneoi/openchat).

pvduy/exp_dpo_3

--- dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 447928177 num_examples: 100121 - name: test num_bytes: 4538037 num_examples: 750 download_size: 240211672 dataset_size: 452466214 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/4bd6ba75

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1337 dataset_size: 182 --- # Dataset Card for "4bd6ba75" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nz/anthropic_hh_rlhf

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 202114406 num_examples: 160800 - name: test num_bytes: 10820339 num_examples: 8552 download_size: 127364682 dataset_size: 212934745 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

CyberHarem/sanjouno_haruhime_isitwrongtotrytopickupgirlsinadungeon

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sanjouno_haruhime (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka) This is the dataset of sanjouno_haruhime (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka), containing 200 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)).

aditijha/instruct_v3_subset

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 3930962.2554168818 num_examples: 1000 download_size: 2374280 dataset_size: 3930962.2554168818 --- # Dataset Card for "instruct_v3_subset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

RealTimeData/math_alltime

--- dataset_info: - config_name: 2017-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 80660853 num_examples: 941 download_size: 9158732 dataset_size: 80660853 - config_name: 2017-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 84851628 num_examples: 910 download_size: 10270205 dataset_size: 84851628 - config_name: 2017-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 65654141 num_examples: 873 download_size: 8389188 dataset_size: 65654141 - config_name: 2017-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 69962303 num_examples: 900 download_size: 8649741 dataset_size: 69962303 - config_name: 2017-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61331035 num_examples: 850 download_size: 7502347 dataset_size: 61331035 - config_name: 2017-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 69089197 num_examples: 857 download_size: 8504218 dataset_size: 69089197 - config_name: 2017-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 65942734 num_examples: 833 download_size: 7792388 dataset_size: 65942734 - config_name: 2017-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 68340459 num_examples: 842 download_size: 8487447 dataset_size: 68340459 - config_name: 2017-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61008346 num_examples: 896 download_size: 7278417 dataset_size: 61008346 - config_name: 2017-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53163267 num_examples: 818 download_size: 6483992 dataset_size: 53163267 - config_name: 2017-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 59760183 num_examples: 808 download_size: 7924709 dataset_size: 59760183 - config_name: 2017-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55924348 num_examples: 836 download_size: 6647153 dataset_size: 55924348 - config_name: 2018-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53423968 num_examples: 804 download_size: 6435279 dataset_size: 53423968 - config_name: 2018-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 56097587 num_examples: 836 download_size: 6786404 dataset_size: 56097587 - config_name: 2018-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 52716955 num_examples: 811 download_size: 6716783 dataset_size: 52716955 - config_name: 2018-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61021658 num_examples: 834 download_size: 7312214 dataset_size: 61021658 - config_name: 2018-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55772617 num_examples: 786 download_size: 7085239 dataset_size: 55772617 - config_name: 2018-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 51150298 num_examples: 749 download_size: 6364046 dataset_size: 51150298 - config_name: 2018-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 54584695 num_examples: 758 download_size: 6726781 dataset_size: 54584695 - config_name: 2018-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55593417 num_examples: 781 download_size: 6974572 dataset_size: 55593417 - config_name: 2018-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 54969100 num_examples: 823 download_size: 6338898 dataset_size: 54969100 - config_name: 2018-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61315262 num_examples: 760 download_size: 6851372 dataset_size: 61315262 - config_name: 2018-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 60746966 num_examples: 716 download_size: 6647704 dataset_size: 60746966 - config_name: 2018-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 66850978 num_examples: 743 download_size: 8017159 dataset_size: 66850978 - config_name: 2019-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 66498129 num_examples: 757 download_size: 7133679 dataset_size: 66498129 - config_name: 2019-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 62762617 num_examples: 727 download_size: 7362944 dataset_size: 62762617 - config_name: 2019-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53635552 num_examples: 722 download_size: 6159124 dataset_size: 53635552 - config_name: 2019-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55324827 num_examples: 711 download_size: 6655057 dataset_size: 55324827 - config_name: 2019-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 56829578 num_examples: 723 download_size: 6558721 dataset_size: 56829578 - config_name: 2019-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 61139555 num_examples: 688 download_size: 7221420 dataset_size: 61139555 - config_name: 2019-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53673145 num_examples: 683 download_size: 6416744 dataset_size: 53673145 - config_name: 2019-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 77910351 num_examples: 747 download_size: 9404169 dataset_size: 77910351 - config_name: 2019-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 63119202 num_examples: 745 download_size: 7318462 dataset_size: 63119202 - config_name: 2019-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 49155709 num_examples: 683 download_size: 5592949 dataset_size: 49155709 - config_name: 2019-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 48224122 num_examples: 709 download_size: 5549457 dataset_size: 48224122 - config_name: 2019-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55688069 num_examples: 710 download_size: 6563642 dataset_size: 55688069 - config_name: 2020-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 53792398 num_examples: 683 download_size: 6403117 dataset_size: 53792398 - config_name: 2020-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 46752243 num_examples: 683 download_size: 5617224 dataset_size: 46752243 - config_name: 2020-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 44255484 num_examples: 650 download_size: 5392729 dataset_size: 44255484 - config_name: 2020-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 49661204 num_examples: 668 download_size: 6130487 dataset_size: 49661204 - config_name: 2020-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 43477816 num_examples: 642 download_size: 5454984 dataset_size: 43477816 - config_name: 2020-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 45100323 num_examples: 633 download_size: 6224900 dataset_size: 45100323 - config_name: 2020-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 68329723 num_examples: 719 download_size: 8616264 dataset_size: 68329723 - config_name: 2020-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 55807634 num_examples: 688 download_size: 6625344 dataset_size: 55807634 - config_name: 2020-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 50288522 num_examples: 679 download_size: 5669747 dataset_size: 50288522 - config_name: 2020-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 43771861 num_examples: 615 download_size: 5445208 dataset_size: 43771861 - config_name: 2020-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 45212400 num_examples: 649 download_size: 5644663 dataset_size: 45212400 - config_name: 2020-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 46070938 num_examples: 630 download_size: 5635182 dataset_size: 46070938 - config_name: 2021-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 56230519 num_examples: 681 download_size: 6937404 dataset_size: 56230519 - config_name: 2021-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 43007105 num_examples: 621 download_size: 5538417 dataset_size: 43007105 - config_name: 2021-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 49678764 num_examples: 689 download_size: 6273745 dataset_size: 49678764 - config_name: 2021-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 45003518 num_examples: 644 download_size: 5524111 dataset_size: 45003518 - config_name: 2021-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 37522647 num_examples: 629 download_size: 4804605 dataset_size: 37522647 - config_name: 2021-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 36752211 num_examples: 558 download_size: 4800667 dataset_size: 36752211 - config_name: 2021-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 34324442 num_examples: 536 download_size: 4535535 dataset_size: 34324442 - config_name: 2021-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 38737457 num_examples: 566 download_size: 4795296 dataset_size: 38737457 - config_name: 2021-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 42672055 num_examples: 593 download_size: 5900612 dataset_size: 42672055 - config_name: 2021-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 27437600 num_examples: 510 download_size: 3653512 dataset_size: 27437600 - config_name: 2021-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25301425 num_examples: 481 download_size: 3579488 dataset_size: 25301425 - config_name: 2021-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25259496 num_examples: 474 download_size: 3480663 dataset_size: 25259496 - config_name: 2022-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 31818974 num_examples: 514 download_size: 4209788 dataset_size: 31818974 - config_name: 2022-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25615543 num_examples: 470 download_size: 3591296 dataset_size: 25615543 - config_name: 2022-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 19714500 num_examples: 444 download_size: 2932476 dataset_size: 19714500 - config_name: 2022-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 23915512 num_examples: 489 download_size: 3243798 dataset_size: 23915512 - config_name: 2022-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 24456246 num_examples: 471 download_size: 3460915 dataset_size: 24456246 - config_name: 2022-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 34130328 num_examples: 550 download_size: 4517837 dataset_size: 34130328 - config_name: 2022-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 33495692 num_examples: 489 download_size: 4148878 dataset_size: 33495692 - config_name: 2022-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 38369082 num_examples: 533 download_size: 4463578 dataset_size: 38369082 - config_name: 2022-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 29245493 num_examples: 513 download_size: 3888463 dataset_size: 29245493 - config_name: 2022-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30693581 num_examples: 476 download_size: 3915331 dataset_size: 30693581 - config_name: 2022-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 22717890 num_examples: 429 download_size: 2909674 dataset_size: 22717890 - config_name: 2022-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 21326790 num_examples: 442 download_size: 3074597 dataset_size: 21326790 - config_name: 2023-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 28678459 num_examples: 484 download_size: 3746107 dataset_size: 28678459 - config_name: 2023-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 34068530 num_examples: 543 download_size: 4468866 dataset_size: 34068530 - config_name: 2023-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 28386987 num_examples: 474 download_size: 3582895 dataset_size: 28386987 - config_name: 2023-04 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 24505237 num_examples: 482 download_size: 3400300 dataset_size: 24505237 - config_name: 2023-05 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30796646 num_examples: 497 download_size: 4010553 dataset_size: 30796646 - config_name: 2023-06 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30563236 num_examples: 474 download_size: 3940672 dataset_size: 30563236 - config_name: 2023-07 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 28593939 num_examples: 496 download_size: 3857623 dataset_size: 28593939 - config_name: 2023-08 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 22784600 num_examples: 426 download_size: 3102013 dataset_size: 22784600 - config_name: 2023-09 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 20901199 num_examples: 392 download_size: 2919138 dataset_size: 20901199 - config_name: 2023-10 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 20846111 num_examples: 404 download_size: 3040637 dataset_size: 20846111 - config_name: 2023-11 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 25367205 num_examples: 460 download_size: 3587527 dataset_size: 25367205 - config_name: 2023-12 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 24516907 num_examples: 412 download_size: 3302967 dataset_size: 24516907 - config_name: 2024-01 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30347026 num_examples: 515 download_size: 4061650 dataset_size: 30347026 - config_name: 2024-02 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 30435603 num_examples: 464 download_size: 3957232 dataset_size: 30435603 - config_name: 2024-03 features: - name: question dtype: string - name: question_id dtype: int64 - name: score dtype: int64 - name: link dtype: string - name: body dtype: string - name: answers list: - name: answer_id dtype: int64 - name: score dtype: int64 - name: text dtype: string - name: verbolised dtype: string splits: - name: train num_bytes: 20921895 num_examples: 397 download_size: 2929840 dataset_size: 20921895 configs: - config_name: 2017-01 data_files: - split: train path: 2017-01/train-* - config_name: 2017-02 data_files: - split: train path: 2017-02/train-* - config_name: 2017-03 data_files: - split: train path: 2017-03/train-* - config_name: 2017-04 data_files: - split: train path: 2017-04/train-* - config_name: 2017-05 data_files: - split: train path: 2017-05/train-* - config_name: 2017-06 data_files: - split: train path: 2017-06/train-* - config_name: 2017-07 data_files: - split: train path: 2017-07/train-* - config_name: 2017-08 data_files: - split: train path: 2017-08/train-* - config_name: 2017-09 data_files: - split: train path: 2017-09/train-* - config_name: 2017-10 data_files: - split: train path: 2017-10/train-* - config_name: 2017-11 data_files: - split: train path: 2017-11/train-* - config_name: 2017-12 data_files: - split: train path: 2017-12/train-* - config_name: 2018-01 data_files: - split: train path: 2018-01/train-* - config_name: 2018-02 data_files: - split: train path: 2018-02/train-* - config_name: 2018-03 data_files: - split: train path: 2018-03/train-* - config_name: 2018-04 data_files: - split: train path: 2018-04/train-* - config_name: 2018-05 data_files: - split: train path: 2018-05/train-* - config_name: 2018-06 data_files: - split: train path: 2018-06/train-* - config_name: 2018-07 data_files: - split: train path: 2018-07/train-* - config_name: 2018-08 data_files: - split: train path: 2018-08/train-* - config_name: 2018-09 data_files: - split: train path: 2018-09/train-* - config_name: 2018-10 data_files: - split: train path: 2018-10/train-* - config_name: 2018-11 data_files: - split: train path: 2018-11/train-* - config_name: 2018-12 data_files: - split: train path: 2018-12/train-* - config_name: 2019-01 data_files: - split: train path: 2019-01/train-* - config_name: 2019-02 data_files: - split: train path: 2019-02/train-* - config_name: 2019-03 data_files: - split: train path: 2019-03/train-* - config_name: 2019-04 data_files: - split: train path: 2019-04/train-* - config_name: 2019-05 data_files: - split: train path: 2019-05/train-* - config_name: 2019-06 data_files: - split: train path: 2019-06/train-* - config_name: 2019-07 data_files: - split: train path: 2019-07/train-* - config_name: 2019-08 data_files: - split: train path: 2019-08/train-* - config_name: 2019-09 data_files: - split: train path: 2019-09/train-* - config_name: 2019-10 data_files: - split: train path: 2019-10/train-* - config_name: 2019-11 data_files: - split: train path: 2019-11/train-* - config_name: 2019-12 data_files: - split: train path: 2019-12/train-* - config_name: 2020-01 data_files: - split: train path: 2020-01/train-* - config_name: 2020-02 data_files: - split: train path: 2020-02/train-* - config_name: 2020-03 data_files: - split: train path: 2020-03/train-* - config_name: 2020-04 data_files: - split: train path: 2020-04/train-* - config_name: 2020-05 data_files: - split: train path: 2020-05/train-* - config_name: 2020-06 data_files: - split: train path: 2020-06/train-* - config_name: 2020-07 data_files: - split: train path: 2020-07/train-* - config_name: 2020-08 data_files: - split: train path: 2020-08/train-* - config_name: 2020-09 data_files: - split: train path: 2020-09/train-* - config_name: 2020-10 data_files: - split: train path: 2020-10/train-* - config_name: 2020-11 data_files: - split: train path: 2020-11/train-* - config_name: 2020-12 data_files: - split: train path: 2020-12/train-* - config_name: 2021-01 data_files: - split: train path: 2021-01/train-* - config_name: 2021-02 data_files: - split: train path: 2021-02/train-* - config_name: 2021-03 data_files: - split: train path: 2021-03/train-* - config_name: 2021-04 data_files: - split: train path: 2021-04/train-* - config_name: 2021-05 data_files: - split: train path: 2021-05/train-* - config_name: 2021-06 data_files: - split: train path: 2021-06/train-* - config_name: 2021-07 data_files: - split: train path: 2021-07/train-* - config_name: 2021-08 data_files: - split: train path: 2021-08/train-* - config_name: 2021-09 data_files: - split: train path: 2021-09/train-* - config_name: 2021-10 data_files: - split: train path: 2021-10/train-* - config_name: 2021-11 data_files: - split: train path: 2021-11/train-* - config_name: 2021-12 data_files: - split: train path: 2021-12/train-* - config_name: 2022-01 data_files: - split: train path: 2022-01/train-* - config_name: 2022-02 data_files: - split: train path: 2022-02/train-* - config_name: 2022-03 data_files: - split: train path: 2022-03/train-* - config_name: 2022-04 data_files: - split: train path: 2022-04/train-* - config_name: 2022-05 data_files: - split: train path: 2022-05/train-* - config_name: 2022-06 data_files: - split: train path: 2022-06/train-* - config_name: 2022-07 data_files: - split: train path: 2022-07/train-* - config_name: 2022-08 data_files: - split: train path: 2022-08/train-* - config_name: 2022-09 data_files: - split: train path: 2022-09/train-* - config_name: 2022-10 data_files: - split: train path: 2022-10/train-* - config_name: 2022-11 data_files: - split: train path: 2022-11/train-* - config_name: 2022-12 data_files: - split: train path: 2022-12/train-* - config_name: 2023-01 data_files: - split: train path: 2023-01/train-* - config_name: 2023-02 data_files: - split: train path: 2023-02/train-* - config_name: 2023-03 data_files: - split: train path: 2023-03/train-* - config_name: 2023-04 data_files: - split: train path: 2023-04/train-* - config_name: 2023-05 data_files: - split: train path: 2023-05/train-* - config_name: 2023-06 data_files: - split: train path: 2023-06/train-* - config_name: 2023-07 data_files: - split: train path: 2023-07/train-* - config_name: 2023-08 data_files: - split: train path: 2023-08/train-* - config_name: 2023-09 data_files: - split: train path: 2023-09/train-* - config_name: 2023-10 data_files: - split: train path: 2023-10/train-* - config_name: 2023-11 data_files: - split: train path: 2023-11/train-* - config_name: 2023-12 data_files: - split: train path: 2023-12/train-* - config_name: 2024-01 data_files: - split: train path: 2024-01/train-* - config_name: 2024-02 data_files: - split: train path: 2024-02/train-* - config_name: 2024-03 data_files: - split: train path: 2024-03/train-* ---

vhtran/en-de-2023

--- license: cc-by-4.0 --- Translate German to English

DBQ/Prada.Product.prices.Italy

--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Italy - Prada - Product-level price list tags: - webscraping - ecommerce - Prada - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: string - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 1274261 num_examples: 2533 download_size: 364017 dataset_size: 1274261 --- # Prada web scraped data ## About the website The **fashion industry in EMEA**, particularly in **Italy**, is long-standing and globally respected, with prestigious fashion houses and excellent craftsmanship. One of the leading fashion brands in Italy is **Prada**, an iconic name synonymous with luxury and style. Prada operates in a competitive space characterized by innovative design, high-quality materials, and cultivating desirability through brand prestige. Lately, the fashion industry, including Prada, has been increasingly moving towards the digital space. With the surge in online shopping trends, **Ecommerce** has become increasingly relevant. The dataset observed provides insights from **Ecommerce product-list page (PLP) data** specific to the Prada brand in the Italian market. ## Link to **dataset** [Italy - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Italy/r/recUq5K9dC8eYLDss)

yanekyuk/wikikey

--- license: mit ---

EleutherAI/proof-pile-2

--- task_categories: - text-generation language: - en tags: - math size_categories: - 10B<n<100B --- <img src="proofpile_logo.jpg" width="500"> [ArXiv](http://arxiv.org/abs/2310.10631) | [Models](https://huggingface.co/EleutherAI/llemma_34b) | [Data](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | [Code](https://github.com/EleutherAI/math-lm) | [Blog](https://blog.eleuther.ai/llemma/) | [Sample Explorer](https://llemma-demo.github.io/) [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Hailey Schoelkopf](https://github.com/haileyschoelkopf), [Keiran Paster](https://keirp.com), [Marco Dos Santos](https://github.com/dsantosmarco), [Stephen McAleer](https://www.andrew.cmu.edu/user/smcaleer/), [Albert Q. Jiang](https://albertqjiang.github.io/), [Jia Deng](https://www.cs.princeton.edu/~jiadeng/), [Stella Biderman](https://www.stellabiderman.com/), [Sean Welleck](https://wellecks.com/) The **Proof-Pile-2** is a 55 billion token dataset of mathematical and scientific documents. This dataset was created in order to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b) models. It consists of three subsets: - `arxiv` (29B tokens): the ArXiv subset of [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) - `open-web-math` (15B tokens): The [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) dataset, which contains much of the high-quality mathematical text from the internet. - `algebraic-stack` (11B tokens): A new dataset of mathematical code, including numerical computing, computer algebra, and formal mathematics. You can download the dataset as follows ```python from datasets import load_dataset ds = load_dataset("EleutherAI/proof-pile-2") # To load only a specific subset, pass it as an argument, e.g ds_arxiv = load_dataset("EleutherAI/proof-pile-2", "arxiv") ``` ### Schema Each dataset row has the following structure ```python { "text": ..., # document text "meta": ..., # JSON string of metadata, schema specific to data source } ``` ### Dataset Contents For detailed documentation of the ArXiv and web subsets, refer to [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math). The following table enumerates the contents of the AlgebraicStack by programming language. The AlgebraicStack is filtered to only include documents that contain mathematics, as judged by hand-crafted, language-specific heuristics. | Language | AlgebraicStack tokens | |-----------|-----------------------| | Agda | 35.2 M | | C | 25.1 M | | C++ | 954.1 M | | Coq | 281.9 M | | Fortran | 724.9 M | | GAP | 3.6 M | | Haskell | 9.1 M | | Idris | 10.9 M | | Isabelle | 1,089.7 M | | Julia | 531.0 M | | Jupyter | 199.1 M | | Lean | 285.6 M | | Maple | 2.0 M | | Matlab | 65.8 M | | Python | 6,098.8 M | | R | 71.3 M | | Tex | 567.7 M | | **Total** | **10,955.7 M** | ### License We do not alter the license of any of the underlying data. ### Version History **v1.1.0**: Contains an updated version of OpenWebMath, precisely the one available at [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math). This version of OpenWebMath has slightly improved filtering, for example, removal of very short documents. **v1.0.0**: The data used to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b). Uses a development version of OpenWebMath. ### Citation For the entire Proof-Pile-2, cite ``` @misc{azerbayev2023llemma, title={Llemma: An Open Language Model For Mathematics}, author={Zhangir Azerbayev and Hailey Schoelkopf and Keiran Paster and Marco Dos Santos and Stephen McAleer and Albert Q. Jiang and Jia Deng and Stella Biderman and Sean Welleck}, year={2023}, eprint={2310.10631}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` For the ArXiv subset, cite ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` For OpenWebMath, cite ``` @misc{paster2023openwebmath, title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text}, author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba}, year={2023}, eprint={2310.06786}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

autoevaluate/autoeval-eval-xsum-default-5381b8-67099145593

--- type: predictions tags: - autotrain - evaluation datasets: - xsum eval_info: task: summarization model: t5-small metrics: [] dataset_name: xsum dataset_config: default dataset_split: test col_mapping: text: document target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: t5-small * Dataset: xsum * 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 [@michaeldesmond](https://huggingface.co/michaeldesmond) for evaluating this model.

joey234/mmlu-moral_disputes-rule-neg

--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 108868 num_examples: 346 download_size: 60737 dataset_size: 108868 --- # Dataset Card for "mmlu-moral_disputes-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DrBenchmark/QUAERO

--- language: - fr license: other multilinguality: monolingual pretty_name: QUAERO homepage: https://quaerofrenchmed.limsi.fr/ task_categories: - token-classification tags: - medical size_categories: - 1K<n<10K --- # Dataset Card for QUAERO ## Dataset Description - **Homepage:** https://quaerofrenchmed.limsi.fr/ - **Pubmed:** True - **Public:** True - **Tasks:** Named-Entity Recognition (NER) The QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3]. A selection of MEDLINE titles and EMEA documents were manually annotated. The annotation process was guided by concepts in the Unified Medical Language System (UMLS): 1. Ten types of clinical entities, as defined by the following UMLS Semantic Groups (Bodenreider and McCray 2003) were annotated: Anatomy, Chemical and Drugs, Devices, Disorders, Geographic Areas, Living Beings, Objects, Phenomena, Physiology, Procedures. 2. The annotations were made in a comprehensive fashion, so that nested entities were marked, and entities could be mapped to more than one UMLS concept. In particular: (a) If a mention can refer to more than one Semantic Group, all the relevant Semantic Groups should be annotated. For instance, the mention “récidive” (recurrence) in the phrase “prévention des récidives” (recurrence prevention) should be annotated with the category “DISORDER” (CUI C2825055) and the category “PHENOMENON” (CUI C0034897); (b) If a mention can refer to more than one UMLS concept within the same Semantic Group, all the relevant concepts should be annotated. For instance, the mention “maniaques” (obsessive) in the phrase “patients maniaques” (obsessive patients) should be annotated with CUIs C0564408 and C0338831 (category “DISORDER”); (c) Entities which span overlaps with that of another entity should still be annotated. For instance, in the phrase “infarctus du myocarde” (myocardial infarction), the mention “myocarde” (myocardium) should be annotated with category “ANATOMY” (CUI C0027061) and the mention “infarctus du myocarde” should be annotated with category “DISORDER” (CUI C0027051) The QUAERO French Medical Corpus BioC release comprises a subset of the QUAERO French Medical corpus, as follows: Training data (BRAT version used in CLEF eHealth 2015 task 1b as training data): - MEDLINE_train_bioc file: 833 MEDLINE titles, annotated with normalized entities in the BioC format - EMEA_train_bioc file: 3 EMEA documents, segmented into 11 sub-documents, annotated with normalized entities in the BioC format Development data (BRAT version used in CLEF eHealth 2015 task 1b as test data and in CLEF eHealth 2016 task 2 as development data): - MEDLINE_dev_bioc file: 832 MEDLINE titles, annotated with normalized entities in the BioC format - EMEA_dev_bioc file: 3 EMEA documents, segmented into 12 sub-documents, annotated with normalized entities in the BioC format Test data (BRAT version used in CLEF eHealth 2016 task 2 as test data): - MEDLINE_test_bioc folder: 833 MEDLINE titles, annotated with normalized entities in the BioC format - EMEA folder_test_bioc: 4 EMEA documents, segmented into 15 sub-documents, annotated with normalized entities in the BioC format This release of the QUAERO French medical corpus, BioC version, comes in the BioC format, through automatic conversion from the original BRAT format obtained with the Brat2BioC tool https://bitbucket.org/nicta_biomed/brat2bioc developped by Jimeno Yepes et al. Antonio Jimeno Yepes, Mariana Neves, Karin Verspoor Brat2BioC: conversion tool between brat and BioC BioCreative IV track 1 - BioC: The BioCreative Interoperability Initiative, 2013 Please note that the original version of the QUAERO corpus distributed in the CLEF eHealth challenge 2015 and 2016 came in the BRAT stand alone format. It was distributed with the CLEF eHealth evaluation tool. This original distribution of the QUAERO French Medical corpus is available separately from https://quaerofrenchmed.limsi.fr All questions regarding the task or data should be addressed to aurelie.neveol@limsi.fr ## Citation Information ``` @InProceedings{neveol14quaero, author = {Névéol, Aurélie and Grouin, Cyril and Leixa, Jeremy and Rosset, Sophie and Zweigenbaum, Pierre}, title = {The {QUAERO} {French} Medical Corpus: A Ressource for Medical Entity Recognition and Normalization}, OPTbooktitle = {Proceedings of the Fourth Workshop on Building and Evaluating Ressources for Health and Biomedical Text Processing}, booktitle = {Proc of BioTextMining Work}, OPTseries = {BioTxtM 2014}, year = {2014}, pages = {24--30}, } ```

Christianevc/fullTestingData

--- license: unknown ---

learn3r/SDG_math

--- dataset_info: features: - name: jargon dtype: string - name: definition dtype: string splits: - name: train num_bytes: 38022 num_examples: 200 download_size: 23657 dataset_size: 38022 --- # Dataset Card for "SDG_math" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-one-sec-cv12/chunk_86

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1377070296 num_examples: 270438 download_size: 1404210357 dataset_size: 1377070296 --- # Dataset Card for "chunk_86" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Codec-SUPERB/cv_13_zh_tw_synth

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: id dtype: string splits: - name: original num_bytes: 1409834613.762 num_examples: 61154 - name: academicodec_hifi_16k_320d num_bytes: 6915926342.0 num_examples: 61154 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 6915926342.0 num_examples: 61154 - name: academicodec_hifi_24k_320d num_bytes: 10379169862.0 num_examples: 61154 - name: audiodec_24k_320d num_bytes: 10413391582.0 num_examples: 61154 - name: dac_16k num_bytes: 6943952982.0 num_examples: 61154 - name: dac_24k num_bytes: 10395499998.0 num_examples: 61154 - name: dac_44k num_bytes: 19097120420.0 num_examples: 61154 - name: encodec_24k num_bytes: 10395562436.0 num_examples: 61154 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 6928228346.0 num_examples: 61154 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 6928231074.0 num_examples: 61154 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 6932025314.0 num_examples: 61154 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 6932025314.0 num_examples: 61154 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 6932025314.0 num_examples: 61154 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 6932025314.0 num_examples: 61154 - name: speech_tokenizer_16k num_bytes: 6946484422.0 num_examples: 61154 download_size: 109934173015 dataset_size: 131397429675.762 configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k path: data/encodec_24k-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* ---

CyberHarem/myrrh_fireemblem

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of myrrh (Fire Emblem) This is the dataset of myrrh (Fire Emblem), containing 247 images and their tags. The core tags of this character are `purple_hair, twintails, wings, dragon_wings, red_eyes, multi-tied_hair, 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 | 247 | 335.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 247 | 190.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 574 | 395.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 247 | 298.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 574 | 549.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/myrrh_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/myrrh_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 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, looking_at_viewer, solo, white_background, closed_mouth, simple_background, smile, dress, dragon_girl | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, dress, sandals, simple_background, solo, white_background, wristband, dragon_girl, full_body, looking_at_viewer, closed_mouth, own_hands_together | | 2 | 31 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, solo, long_sleeves, fake_animal_ears, halloween_costume, bat_ears, fur_trim, dress, simple_background, open_mouth, white_background | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, nipples, nude, pussy, small_breasts, solo, navel, loli, spread_legs | | 4 | 5 | ![](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, hetero, navel, nipples, open_mouth, small_breasts, solo_focus, blush, mosaic_censoring, sex, vaginal, 1boy, loli, nude, pussy, spread_legs, tears, 3boys, dragon_girl, multiple_penises, panties_around_one_leg | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | white_background | closed_mouth | simple_background | smile | dress | dragon_girl | sandals | wristband | full_body | own_hands_together | long_sleeves | fake_animal_ears | halloween_costume | bat_ears | fur_trim | open_mouth | blush | nipples | nude | pussy | small_breasts | navel | loli | spread_legs | hetero | solo_focus | mosaic_censoring | sex | vaginal | 1boy | tears | 3boys | multiple_penises | panties_around_one_leg | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:---------------|:--------------------|:--------|:--------|:--------------|:----------|:------------|:------------|:---------------------|:---------------|:-------------------|:--------------------|:-----------|:-----------|:-------------|:--------|:----------|:-------|:--------|:----------------|:--------|:-------|:--------------|:---------|:-------------|:-------------------|:------|:----------|:-------|:--------|:--------|:-------------------|:-------------------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 31 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | | X | | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | 4 | 5 | ![](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 |

healthcorum/autotrain-data-anm2-25mh-u5jo

--- dataset_info: features: - name: target dtype: string - name: autotrain_text dtype: string splits: - name: train num_bytes: 36008183 num_examples: 9998 - name: validation num_bytes: 36008183 num_examples: 9998 download_size: 11959104 dataset_size: 72016366 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "autotrain-data-anm2-25mh-u5jo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/jaguar_warrior_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jaguar_warrior/ジャガーマン/豹人 (Fate/Grand Order) This is the dataset of jaguar_warrior/ジャガーマン/豹人 (Fate/Grand Order), containing 29 images and their tags. The core tags of this character are `animal_ears, short_hair, orange_hair, brown_eyes, brown_hair, tail, fang, 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 | 29 | 35.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jaguar_warrior_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 29 | 31.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jaguar_warrior_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 70 | 62.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jaguar_warrior_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/jaguar_warrior_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 | 29 | ![](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) | solo, open_mouth, 1girl, looking_at_viewer, smile, hood, animal_costume, holding, blush, tiger_print | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | solo | open_mouth | 1girl | looking_at_viewer | smile | hood | animal_costume | holding | blush | tiger_print | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:-------------|:--------|:--------------------|:--------|:-------|:-----------------|:----------|:--------|:--------------| | 0 | 29 | ![](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 |

krinal/embeddings_state_of_union

--- license: apache-2.0 --- Embeddings generated from english text corpus file. Model used: sentence-transformers/all-MiniLM-L6-v2

nguyenminh871/reentrancy_solidity_function

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: func dtype: string - name: target dtype: bool - name: project dtype: string splits: - name: train num_bytes: 840896 num_examples: 3203 download_size: 156960 dataset_size: 840896 --- # Dataset Card for "reentrancy_solidity_function" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nadav/pixel_glue_stsb_low_noise

--- dataset_info: features: - name: image dtype: image - name: label dtype: float32 splits: - name: validation num_bytes: 39630800.5 num_examples: 1500 download_size: 39537172 dataset_size: 39630800.5 --- # Dataset Card for "pixel_glue_stsb_low_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bigbio/med_qa

--- language: - en - zh bigbio_language: - English - Chinese (Simplified) - Chinese (Traditional, Taiwan) license: unknown multilinguality: multilingual bigbio_license_shortname: UNKNOWN pretty_name: MedQA homepage: https://github.com/jind11/MedQA bigbio_pubmed: False bigbio_public: True bigbio_tasks: - QUESTION_ANSWERING --- # Dataset Card for MedQA ## Dataset Description - **Homepage:** https://github.com/jind11/MedQA - **Pubmed:** False - **Public:** True - **Tasks:** QA In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading comprehension models can obtain necessary knowledge for answering the questions. ## Citation Information ``` @article{jin2021disease, title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={Applied Sciences}, volume={11}, number={14}, pages={6421}, year={2021}, publisher={MDPI} } ```

flpelerin/openorca-alpaca-15k

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

ostapeno/flanv2_100k

--- license: apache-2.0 dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 147796259 num_examples: 100000 download_size: 85036882 dataset_size: 147796259 ---

liuyanchen1015/MULTI_VALUE_mrpc_aint_have

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 4455 num_examples: 17 - name: train num_bytes: 12459 num_examples: 45 - name: validation num_bytes: 1262 num_examples: 5 download_size: 23628 dataset_size: 18176 --- # Dataset Card for "MULTI_VALUE_mrpc_aint_have" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gigant/tib_complete_metadata

--- dataset_info: features: - name: title dtype: string - name: href dtype: string - name: description dtype: 'null' - name: url_vid dtype: string - name: release_date dtype: string - name: subject dtype: string - name: genre dtype: string - name: abstract dtype: string - name: language dtype: string - name: doi dtype: string - name: license dtype: string - name: author dtype: string - name: contributors dtype: string splits: - name: train num_bytes: 30171096 num_examples: 22091 download_size: 11964701 dataset_size: 30171096 --- # Dataset Card for "tib_complete_metadata" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_FredrikBL__NeuralPipe-7B-slerp

--- pretty_name: Evaluation run of FredrikBL/NeuralPipe-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FredrikBL/NeuralPipe-7B-slerp](https://huggingface.co/FredrikBL/NeuralPipe-7B-slerp)\ \ 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 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 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_FredrikBL__NeuralPipe-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T00:21:59.975641](https://huggingface.co/datasets/open-llm-leaderboard/details_FredrikBL__NeuralPipe-7B-slerp/blob/main/results_2024-03-22T00-21-59.975641.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\": 0.6453218800457399,\n\ \ \"acc_stderr\": 0.03212887690836472,\n \"acc_norm\": 0.6457679471487517,\n\ \ \"acc_norm_stderr\": 0.032784859928949854,\n \"mc1\": 0.4283965728274174,\n\ \ \"mc1_stderr\": 0.017323088597314754,\n \"mc2\": 0.598389086821388,\n\ \ \"mc2_stderr\": 0.015156739153282793\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.64419795221843,\n \"acc_stderr\": 0.013990571137918762,\n\ \ \"acc_norm\": 0.6757679180887372,\n \"acc_norm_stderr\": 0.013678810399518827\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6697868950408286,\n\ \ \"acc_stderr\": 0.004693285694663837,\n \"acc_norm\": 0.8618801035650269,\n\ \ \"acc_norm_stderr\": 0.003443206472757467\n },\n \"harness|hendrycksTest-abstract_algebra|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-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\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\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\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\ : 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\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\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\ \ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055263,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055263\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603346,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603346\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6538461538461539,\n \"acc_stderr\": 0.02412112541694119,\n \ \ \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.02412112541694119\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683512,\n \ \ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683512\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977927,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977927\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\ acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n \"\ acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233494,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233494\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841407\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\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508287,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508287\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.36201117318435755,\n\ \ \"acc_stderr\": 0.016073067350153087,\n \"acc_norm\": 0.36201117318435755,\n\ \ \"acc_norm_stderr\": 0.016073067350153087\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\ \ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\ \ \"acc_stderr\": 0.025403832978179604,\n \"acc_norm\": 0.7234726688102894,\n\ \ \"acc_norm_stderr\": 0.025403832978179604\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4726205997392438,\n\ \ \"acc_stderr\": 0.012751075788015057,\n \"acc_norm\": 0.4726205997392438,\n\ \ \"acc_norm_stderr\": 0.012751075788015057\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.01890101532209309,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.01890101532209309\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174934,\n\ \ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174934\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\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\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4283965728274174,\n\ \ \"mc1_stderr\": 0.017323088597314754,\n \"mc2\": 0.598389086821388,\n\ \ \"mc2_stderr\": 0.015156739153282793\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8011049723756906,\n \"acc_stderr\": 0.011218629972515303\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6868840030326004,\n \ \ \"acc_stderr\": 0.012774285669385084\n }\n}\n```" repo_url: https://huggingface.co/FredrikBL/NeuralPipe-7B-slerp 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_21T22_20_22.252622 path: - '**/details_harness|arc:challenge|25_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|arc:challenge|25_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-22T00-21-59.975641.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|gsm8k|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|gsm8k|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hellaswag|10_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hellaswag|10_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T22-20-22.252622.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-21-59.975641.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T00-21-59.975641.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T00-21-59.975641.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T22_20_22.252622 path: - '**/details_harness|winogrande|5_2024-03-21T22-20-22.252622.parquet' - split: 2024_03_22T00_21_59.975641 path: - '**/details_harness|winogrande|5_2024-03-22T00-21-59.975641.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-22T00-21-59.975641.parquet' - config_name: results data_files: - split: 2024_03_21T22_20_22.252622 path: - results_2024-03-21T22-20-22.252622.parquet - split: 2024_03_22T00_21_59.975641 path: - results_2024-03-22T00-21-59.975641.parquet - split: latest path: - results_2024-03-22T00-21-59.975641.parquet --- # Dataset Card for Evaluation run of FredrikBL/NeuralPipe-7B-slerp  Dataset automatically created during the evaluation run of model [FredrikBL/NeuralPipe-7B-slerp](https://huggingface.co/FredrikBL/NeuralPipe-7B-slerp) 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 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 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_FredrikBL__NeuralPipe-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T00:21:59.975641](https://huggingface.co/datasets/open-llm-leaderboard/details_FredrikBL__NeuralPipe-7B-slerp/blob/main/results_2024-03-22T00-21-59.975641.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": 0.6453218800457399, "acc_stderr": 0.03212887690836472, "acc_norm": 0.6457679471487517, "acc_norm_stderr": 0.032784859928949854, "mc1": 0.4283965728274174, "mc1_stderr": 0.017323088597314754, "mc2": 0.598389086821388, "mc2_stderr": 0.015156739153282793 }, "harness|arc:challenge|25": { "acc": 0.64419795221843, "acc_stderr": 0.013990571137918762, "acc_norm": 0.6757679180887372, "acc_norm_stderr": 0.013678810399518827 }, "harness|hellaswag|10": { "acc": 0.6697868950408286, "acc_stderr": 0.004693285694663837, "acc_norm": 0.8618801035650269, "acc_norm_stderr": 0.003443206472757467 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "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": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "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": 0.6473988439306358, "acc_stderr": 0.036430371689585475, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.036430371689585475 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5872340425531914, "acc_stderr": 0.03218471141400351, "acc_norm": 0.5872340425531914, "acc_norm_stderr": 0.03218471141400351 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.047028804320496165, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.047028804320496165 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055263, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055263 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723292, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723292 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603346, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603346 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6538461538461539, "acc_stderr": 0.02412112541694119, "acc_norm": 0.6538461538461539, "acc_norm_stderr": 0.02412112541694119 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3111111111111111, "acc_stderr": 0.028226446749683512, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.028226446749683512 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977927, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977927 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8513761467889909, "acc_stderr": 0.015251253773660834, "acc_norm": 0.8513761467889909, "acc_norm_stderr": 0.015251253773660834 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078966, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078966 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.025530100460233494, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.025530100460233494 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7786259541984732, "acc_stderr": 0.03641297081313729, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313729 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841407, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841407 }, "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": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508287, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508287 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.36201117318435755, "acc_stderr": 0.016073067350153087, "acc_norm": 0.36201117318435755, "acc_norm_stderr": 0.016073067350153087 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7450980392156863, "acc_stderr": 0.02495418432487991, "acc_norm": 0.7450980392156863, "acc_norm_stderr": 0.02495418432487991 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7234726688102894, "acc_stderr": 0.025403832978179604, "acc_norm": 0.7234726688102894, "acc_norm_stderr": 0.025403832978179604 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4726205997392438, "acc_stderr": 0.012751075788015057, "acc_norm": 0.4726205997392438, "acc_norm_stderr": 0.012751075788015057 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462927, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462927 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6781045751633987, "acc_stderr": 0.01890101532209309, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.01890101532209309 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7551020408163265, "acc_stderr": 0.027529637440174934, "acc_norm": 0.7551020408163265, "acc_norm_stderr": 0.027529637440174934 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "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": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.4283965728274174, "mc1_stderr": 0.017323088597314754, "mc2": 0.598389086821388, "mc2_stderr": 0.015156739153282793 }, "harness|winogrande|5": { "acc": 0.8011049723756906, "acc_stderr": 0.011218629972515303 }, "harness|gsm8k|5": { "acc": 0.6868840030326004, "acc_stderr": 0.012774285669385084 } } ``` ## 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]

pioivenium/im-map-dataset-test

--- license: openrail language: - en pretty_name: map_test size_categories: - 10K<n<100K ---

Arthuerwang/Downsampled_imbd_dataset

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 16760000.0 num_examples: 10000 - name: test num_bytes: 1676000.0 num_examples: 1000 download_size: 0 dataset_size: 18436000.0 --- # Dataset Card for "Downsampled_imbd_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AlekseyKorshuk/chai-experiment-v1-chatml

--- dataset_info: features: - name: source dtype: string - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 2519356815.0 num_examples: 499663 download_size: 1321137823 dataset_size: 2519356815.0 --- # Dataset Card for "chai-experiment-v1-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)