Split (1)

train · 69.3k rows

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open-llm-leaderboard/details_facebook__opt-66b	2023-09-09T17:37:29.000Z	[ "region:us" ]	open-llm-leaderboard	null	null	null	0	--- pretty_name: Evaluation run of facebook/opt-66b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [facebook/opt-66b](https://huggingface.co/facebook/opt-66b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 4 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_facebook__opt-66b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-09T17:37:15.988083](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-66b/blob/main/results_2023-09-09T17-37-15.988083.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\": 0.0012583892617449664,\n\ \ \"em_stderr\": 0.0003630560893119099,\n \"f1\": 0.05010906040268461,\n\ \ \"f1_stderr\": 0.0011683256689483288,\n \"acc\": 0.3557255891520507,\n\ \ \"acc_stderr\": 0.007899506862712421\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.0003630560893119099,\n\ \ \"f1\": 0.05010906040268461,\n \"f1_stderr\": 0.0011683256689483288\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.011372251705837756,\n \ \ \"acc_stderr\": 0.002920666198788773\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7000789265982637,\n \"acc_stderr\": 0.012878347526636068\n\ \ }\n}\n```" repo_url: https://huggingface.co/facebook/opt-66b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|arc:challenge\|25_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|arc:challenge\|25_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-24T00:29:23.220857.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_09T17_37_15.988083 path: - '/details_harness\|drop\|3_2023-09-09T17-37-15.988083.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-09T17-37-15.988083.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_09T17_37_15.988083 path: - '/details_harness\|gsm8k\|5_2023-09-09T17-37-15.988083.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-09T17-37-15.988083.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hellaswag\|10_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hellaswag\|10_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-23T18:07:59.118983.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T00:29:23.220857.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-24T00:29:23.220857.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_23T18_07_59.118983 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-23T18:07:59.118983.parquet' - split: 2023_08_24T00_29_23.220857 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-24T00:29:23.220857.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-24T00:29:23.220857.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_09T17_37_15.988083 path: - '/details_harness\|winogrande\|5_2023-09-09T17-37-15.988083.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-09T17-37-15.988083.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:anatomy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:astronomy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:business_ethics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_biology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_medicine\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_physics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:computer_security\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:econometrics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:formal_logic\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:global_facts\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:human_aging\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:international_law\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:machine_learning\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:management\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:marketing\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:nutrition\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:philosophy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:prehistory\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_law\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:public_relations\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:security_studies\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:sociology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:virology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:world_religions\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:anatomy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:astronomy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:business_ethics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_biology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_medicine\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:college_physics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:computer_security\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:econometrics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:formal_logic\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:global_facts\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:human_aging\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:international_law\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:machine_learning\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:management\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:marketing\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:nutrition\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:philosophy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:prehistory\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_law\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:public_relations\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:security_studies\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:sociology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:virology\|5_2023-08-28T21:15:14.969062.parquet' - '/details_original\|mmlu:world_religions\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:abstract_algebra\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:anatomy\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:anatomy\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:astronomy\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:astronomy\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:business_ethics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:business_ethics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:clinical_knowledge\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:college_biology\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:college_biology\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:college_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:college_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:college_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:college_medicine\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:college_medicine\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:college_physics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:college_physics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:computer_security\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:computer_security\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:conceptual_physics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:econometrics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:econometrics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:electrical_engineering\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:elementary_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:formal_logic\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:formal_logic\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:global_facts\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:global_facts\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_biology\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_chemistry\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_computer_science\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_european_history\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_geography\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_government_and_politics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_macroeconomics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_mathematics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_microeconomics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_physics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_psychology\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_statistics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_us_history\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:high_school_world_history\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:human_aging\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:human_aging\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:human_sexuality\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:international_law\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:international_law\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:jurisprudence\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:logical_fallacies\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:machine_learning\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:machine_learning\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:management\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:management\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:marketing\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:marketing\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:medical_genetics\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:miscellaneous\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:moral_disputes\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:moral_scenarios\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:nutrition\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:nutrition\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:philosophy\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:philosophy\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:prehistory\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:prehistory\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:professional_accounting\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:professional_law\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:professional_law\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:professional_medicine\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:professional_psychology\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:public_relations\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:public_relations\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:security_studies\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:security_studies\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:sociology\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:sociology\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:us_foreign_policy\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:virology\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:virology\|5_2023-08-28T21:15:14.969062.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T21_15_14.969062 path: - '/details_original\|mmlu:world_religions\|5_2023-08-28T21:15:14.969062.parquet' - split: latest path: - '/details_original\|mmlu:world_religions\|5_2023-08-28T21:15:14.969062.parquet' - config_name: results data_files: - split: 2023_08_23T18_07_59.118983 path: - results_2023-08-23T18:07:59.118983.parquet - split: 2023_08_24T00_29_23.220857 path: - results_2023-08-24T00:29:23.220857.parquet - split: 2023_08_28T21_15_14.969062 path: - results_2023-08-28T21:15:14.969062.parquet - split: 2023_09_09T17_37_15.988083 path: - results_2023-09-09T17-37-15.988083.parquet - split: latest path: - results_2023-09-09T17-37-15.988083.parquet --- # Dataset Card for Evaluation run of facebook/opt-66b ## Dataset Description - Homepage: - Repository: https://huggingface.co/facebook/opt-66b - 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 [facebook/opt-66b](https://huggingface.co/facebook/opt-66b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 4 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_facebook__opt-66b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-09T17:37:15.988083](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-66b/blob/main/results_2023-09-09T17-37-15.988083.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": 0.0012583892617449664, "em_stderr": 0.0003630560893119099, "f1": 0.05010906040268461, "f1_stderr": 0.0011683256689483288, "acc": 0.3557255891520507, "acc_stderr": 0.007899506862712421 }, "harness\|drop\|3": { "em": 0.0012583892617449664, "em_stderr": 0.0003630560893119099, "f1": 0.05010906040268461, "f1_stderr": 0.0011683256689483288 }, "harness\|gsm8k\|5": { "acc": 0.011372251705837756, "acc_stderr": 0.002920666198788773 }, "harness\|winogrande\|5": { "acc": 0.7000789265982637, "acc_stderr": 0.012878347526636068 } } ``` ### 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]
Resizable/ECKOSOLDIER	2023-08-23T18:13:19.000Z	[ "license:openrail", "region:us" ]	Resizable	null	null	null	0	--- license: openrail ---
atharvapawar/securix_rules_v1_dataset	2023-08-23T19:48:05.000Z	[ "region:us" ]	atharvapawar	null	null	null	0	Entry not found
fastian1/BraTS20_flair_axial	2023-08-23T21:50:18.000Z	[ "region:us" ]	fastian1	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 131818.0 num_examples: 1 - name: validation num_bytes: 131818.0 num_examples: 1 download_size: 84826 dataset_size: 263636.0 --- # Dataset Card for "BraTS20_flair_axial" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Smahi98/datasetbdarija	2023-08-23T19:52:15.000Z	[ "region:us" ]	Smahi98	null	null	null	0	Entry not found
ShipraS12/imagefolder	2023-08-23T20:05:52.000Z	[ "region:us" ]	ShipraS12	null	null	null	0	Entry not found
gwlms/biofid	2023-08-23T20:44:08.000Z	[ "license:cc-by-4.0", "region:us" ]	gwlms	# Introduction The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23% F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts. # Dataset ## Corpus The BIOfid Corpus is a collection of historical scientific books on central Euro- pean biodiversity. It was assembled by a group of German domain experts, denoting a potential pool of relevant print-only journals and publications for historical biodiversity science. However, mainly due to license issues, not all publications could be considered for the corpus. The available publications were scanned by an external service and subsequently paginated with the software Visual Library. Subsequently, every high-resolution page (400 dpi) was digitized with ABBYY FineReader 8.0 (2005) to ABBYY-XML, which includes structural information like para- graphs, bold/italic text, images, and table blocks. ## Named Entities (NEs) NEs are real-world objects in a given natural language text which denote a unique individual with a proper name (e.g. Frankfurt, Africa, Linnaeus, BHL). This stands in contrast to the class of common names which refer to some kind of entities (e.g. city, continent, person, cor- poration) and not a uniquely identifiable object. The standard task of NER focuses on the former class of proper names. However, it is often not easy to differentiate between both classes. Hence, to support the annotators in making the right decision, we created guidelines which demonstrated the rules for annotations. We gradually developed this document in collaboration with the annotators, until finalizing it as the guidelines for annotating the BIOfid corpus. As we essentially extend the standard task of NER to our scope of biodiversity, our guidelines are built upon those used for producing the Ger- mEval dataset (Benikova et al., 2014). For this, we take the original German text and extend it with the important adjustments described in the next paragraphs for the context of biodiversity. In contrast to Benikova et al. (2014), we do not consider derivative or partial NEs as a separate category. As the recent work of Ahmed an Mehler (2018) has shown, discarding subtle details is even beneficial, whereas fine-graded feature engineering for deep neural networks usually deteriorates the final per- formance. ## Data Format We use the 4-column CoNLL-format which writes each word of a sentence horizontally along its lemma, POS tag and gold label, separating each sentence by an empty new line. For the tag- ging scheme, we opt for BIO (IOB2). The entities ORGANIZATION, OTHER, TIME, PERSON, LOCATION, TAXON are marked by our team of annotators for a given sentence from the BIOfid corpus. We split the BIOfid dataset into train, dev, test files by the common ratio of 80:10:10 percentages after randomizing its order of sentences.	@inproceedings{ahmed-etal-2019-biofid, title = "{BIO}fid Dataset: Publishing a {G}erman Gold Standard for Named Entity Recognition in Historical Biodiversity Literature", author = "Ahmed, Sajawel and Stoeckel, Manuel and Driller, Christine and Pachzelt, Adrian and Mehler, Alexander", booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/K19-1081", doi = "10.18653/v1/K19-1081", pages = "871--880", abstract = "The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23{\%} F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.", }	null	0	--- license: cc-by-4.0 ---
DataProvenanceInitiative/Commercial-Flan-Collection-Dialog	2023-08-23T20:48:51.000Z	[ "region:us" ]	DataProvenanceInitiative	null	null	null	0	Entry not found
DataProvenanceInitiative/Commercial-Flan-Collection-Flan-2021	2023-08-23T20:54:51.000Z	[ "region:us" ]	DataProvenanceInitiative	null	null	null	0	Entry not found
DataProvenanceInitiative/Commercial-Flan-Collection-P3	2023-08-23T20:59:11.000Z	[ "region:us" ]	DataProvenanceInitiative	null	null	null	0	Entry not found
mrm8488/diffusiondb_2m_random_50k	2023-08-23T22:10:13.000Z	[ "region:us" ]	mrm8488	null	null	null	0	Entry not found
marup/PoronChanRVC150Epochs	2023-08-23T21:49:26.000Z	[ "license:openrail", "region:us" ]	marup	null	null	null	0	--- license: openrail ---
JJinBBangMan/distilbert-base-uncased-finetuned-imdb-accelerate	2023-08-23T22:11:02.000Z	[ "region:us" ]	JJinBBangMan	null	null	null	0	Entry not found
shawarmas/modernmesfia	2023-09-01T13:24:49.000Z	[ "region:us" ]	shawarmas	null	null	null	0	Entry not found
quocanh34/soict_train_non_value	2023-08-23T22:53:54.000Z	[ "region:us" ]	quocanh34	null	null	null	0	--- dataset_info: features: - name: id dtype: string - name: sentence dtype: string - name: intent dtype: string - name: sentence_annotation dtype: string - name: entities list: - name: type dtype: string - name: filler dtype: string - name: file dtype: string - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: origin_transcription dtype: string - name: w2v2_transcription dtype: string - name: w2v2_WER dtype: int64 splits: - name: train num_bytes: 6729016.570627503 num_examples: 13 download_size: 638628 dataset_size: 6729016.570627503 --- # Dataset Card for "soict_train_non_value" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davidggphy/voxpopuli_nl_validation	2023-08-23T23:11:58.000Z	[ "region:us" ]	davidggphy	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: sequence: float32 - name: speaker_embeddings sequence: float32 splits: - name: train num_bytes: 181816747.2 num_examples: 1107 - name: test num_bytes: 20201860.8 num_examples: 123 download_size: 201927043 dataset_size: 202018608.0 --- # Dataset Card for "voxpopuli_nl_validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ShapeNet/PartNet-archive	2023-09-20T15:02:22.000Z	[ "language:en", "license:other", "3D shapes", "region:us" ]	ShapeNet	null	null	null	1	--- language: - en pretty_name: PartNet tags: - 3D shapes license: other extra_gated_heading: Acknowledge license to accept the repository extra_gated_prompt: >- To request access to the PartNet repo, you will need to provide your full name (please provide both your first and last name), the name of your advisor or the principal investigator (PI) of your lab (in the PI/Advisor) fields, and the school or company that you are affiliated with (the Affiliation field). After requesting access to the PartNet repo, you will be considered for access approval. After access approval, you (the "Researcher") receive permission to use the PartNet database (the "Database") at Princeton University and Stanford University. In exchange for being able to join the ShapeNet community and receive such permission, Researcher hereby agrees to the following terms and conditions: Researcher shall use the Database only for non-commercial research and educational purposes. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify Princeton University and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted 3D models that he or she may create from the Database. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. The law of the State of New Jersey shall apply to all disputes under this agreement. For access to the data, please fill in your full name (both first and last name), the name of your advisor or principal investigator (PI), and the name of the school or company you are affliated with. Please actually fill out the fields (DO NOT put the word "Advisor" for PI/Advisor and the word "School" for "Affiliation", please specify the name of your advisor and the name of your school). extra_gated_fields: Name: text PI/Advisor: text Affiliation: text Purpose: text Country: text I agree to use this dataset for non-commercial use ONLY: checkbox --- This repository contains archives (zip files) for [PartNet](https://partnet.cs.stanford.edu/), a subset of [ShapeNet](https://shapenet.org) with part annotations. The PartNet prerelease v0 (March 29, 2019) consists of the following: - PartNet v0 annotations (meshes, point clouds, and visualizations) in chunks: data_v0_chunk.zip (302MB), data_v0_chunk.z01-z10 (10GB each) - HDF5 files for the semantic segmentation task (Sec 5.1 of PartNet paper): sem_seg_h5.zip (8GB) - HDF5 files for the instance segmentation task (Sec 5.3 of PartNet paper): ins_seg_h5.zip (20GB) If you use PartNet and ShapeNet data, you agree to abide by the [ShapeNet terms of use](https://shapenet.org/terms). You are only allowed to redistribute the data to your research associates and colleagues provided that they first agree to be bound by these terms and conditions. If you use this data, please cite the main ShapeNet technical report and the PartNet paper. ``` @techreport{shapenet2015, title = {{ShapeNet: An Information-Rich 3D Model Repository}}, author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher}, number = {arXiv:1512.03012 [cs.GR]}, institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago}, year = {2015} } @inproceedings{mo2019partnet, title={{PartNet}: A large-scale benchmark for fine-grained and hierarchical part-level {3D} object understanding}, author={Mo, Kaichun and Zhu, Shilin and Chang, Angel X and Yi, Li and Tripathi, Subarna and Guibas, Leonidas J and Su, Hao}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, pages={909--918}, year={2019} } ``` If you have any questions, please post issues on the [PartNet github issue page](https://github.com/daerduoCarey/partnet_dataset). If you have general feedbacks, please fill in [this form](https://docs.google.com/forms/d/e/1FAIpQLSetsP7aj-Hy0gvP2FxRT3aTIrc_IMqSqR-5Xl8P3x2awDkQbw/viewform?usp=sf_link) to let us know. If you observe any data annotation error, please fill in [this errata](https://docs.google.com/spreadsheets/d/1Q_6r9EblZdP9Grhhm2ob4u0FQ8xurAThlgK-qAcjYP0/edit#gid=0) to help improve PartNet.
jsonfin17/hub24-financial-conversation-sample1	2023-09-07T03:36:55.000Z	[ "region:us" ]	jsonfin17	null	null	null	0	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {viewer: true} --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Financial conversation with the provided customer profile ### 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]
corbt/unlabeled-recipies	2023-08-23T23:43:22.000Z	[ "region:us" ]	corbt	null	null	null	0	--- dataset_info: features: - name: recipe dtype: string splits: - name: train num_bytes: 2793853 num_examples: 5000 download_size: 1465640 dataset_size: 2793853 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "unlabeled-recipies" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Cyleux/job-stateapi-sky-all1	2023-08-23T23:47:54.000Z	[ "region:us" ]	Cyleux	null	null	null	0	Entry not found
Satu4453/Satu4453	2023-09-14T07:15:44.000Z	[ "region:us" ]	Satu4453	null	null	null	0	Entry not found
Aisa463/Aisa463	2023-08-24T00:19:33.000Z	[ "region:us" ]	Aisa463	null	null	null	0	Entry not found
JihyukKim/eli5_subquestion	2023-08-24T03:52:57.000Z	[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "region:us" ]	JihyukKim	null	null	null	0	--- task_categories: - text-generation language: - en size_categories: - 1K<n<10K dataset_info: features: - name: sample_id dtype: string - name: question dtype: string - name: gold_claims sequence: string - name: search_session_samples sequence: - name: turn_quality sequence: - name: query dtype: string - name: answer dtype: string - name: claims_nli dtype: float32 - name: citation_recall dtype: float32 - name: citation_precision dtype: float32 - name: success_claims sequence: string - name: success_cite_sents sequence: string - name: fail_cite_sents sequence: string - name: overall_quality struct: - name: claims_nli dtype: float32 - name: citation_recall dtype: float32 - name: citation_precision dtype: float32 splits: - name: train num_bytes: 879178667 num_examples: 47189 - name: test num_bytes: 18749419 num_examples: 1000 - name: train_small num_bytes: 9489899 num_examples: 512 - name: test_small num_bytes: 2421264 num_examples: 128 download_size: 356075671 dataset_size: 909839249 ---
Maekawa456/Maekawa456	2023-09-19T01:46:20.000Z	[ "region:us" ]	Maekawa456	null	null	null	0	Entry not found
Miyoko35/Miyoko35	2023-08-24T00:27:49.000Z	[ "region:us" ]	Miyoko35	null	null	null	0	Entry not found
Eskobar/NepaliHandSign	2023-08-24T00:42:26.000Z	[ "license:creativeml-openrail-m", "region:us" ]	Eskobar	null	null	null	0	--- license: creativeml-openrail-m ---
madhurbehl/RACECAR_DATA	2023-08-24T01:16:03.000Z	[ "task_categories:robotics", "task_categories:object-detection", "size_categories:10K<n<100K", "language:en", "license:cc", "Autonomous Racing", "Autonomous Vehicles", "Perception", "arxiv:2306.03252", "region:us" ]	madhurbehl	null	null	null	0	--- license: cc task_categories: - robotics - object-detection language: - en tags: - Autonomous Racing - Autonomous Vehicles - Perception pretty_name: racecar size_categories: - 10K<n<100K --- # RACECAR Dataset Welcome to the RACECAR dataset! The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the [Indy Autonomous Challenge](https://www.indyautonomouschallenge.com) during 2021-22 have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing and is suitable to explore issues regarding localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping that arise at the limits of operation of the autonomous vehicle. ### [RACECAR Data Video Demo:] <a href="http://www.youtube.com/watch?v=h3pEPBt8iaY" target="_blank">![RVIZ LiDAR Viz](http://img.youtube.com/vi/h3pEPBt8iaY/0.jpg)</a> This repository describes how the data was collected, how to download the data, its format and organization in ROS2 and NuScenes, as well as helper scripts used to parse the dataset, custom ros messages describing GNSS/IMU/Radar data, and a conversion script that converts ros2 bags to <a href="https://www.nuscenes.org/nuscenes" target="_blank">nuScenes</a> json files. ## Overview - [Data Collection](#data-collection) - [Data Usage and Availability](#data-usage-and-availability) - [Data Usage and Licensce](#data-usage-and-license) - [Citation](#citation) - [Availability](#availability) - [Data Organization](#data-organization) - [Scenario Description](#scenario-description) - [Coordinate Conventions](#coordinate-conventions) - [RACECAR ROS2 - Data Structure](#racecar-ros2---data-structure) - [Folder Structure](#folder-structure) - [Data Processing](#data-processing) - [Topic List](#topic-list) - [RACECAR nuScenes - Data Structure](#racecar-nuscenes---data-structure) - [Folder Structure](#folder-structure-1) - [Tutorials](#tutorials) - [Tutorial 1: Visualization](#tutorial-1-ros2-visualization) - [Custom ROS2 Messages](#installation-of-custom-ros2-messages) - [RVIZ](#visualization-in-rviz) - [Tutorial 2: Localization](#tutorial-2-ros2-localization) - [Tutorial 3: nuScenes](#tutorial-3-nuscenes-jupyter-notebook) - [Acknowledgements](#acknowledgement) ## Data Collection The RACECAR dataset is compiled by contributions from several teams, all of whom competed in the inaugural season of the Indy Autonomous Challenge during 2021-22. Nine university teams participated in two races. The first race was held at the Indianapolis Motor Speedway (IMS) track in Indiana, USA in October 2021, and the second race was held at Las Vegas Motor Speedway (LVMS) in January 2022. At IMS, teams reached speeds up to 150 mph on straights and 136 mph in turns, competing in solo vehicle time trials and obstacle avoidance. At LVMS, teams participated in a head-to-head overtaking competition reaching speeds in excess of 150 mph, with the fastest overtake taking place at 170 mph. The AV-21 Indy Lights vehicle is outfitted with three radars, six pinhole cameras, and three solid- state LiDARs. Each of the sensor modalities covers a 360- degree field of view around the vehicle. For localization, the vehicle is equipped with two sets of high-precision Real-Time Kinematic (RTK) GNSS receivers and IMU. The nine teams that participated were: \|Team\|Initial\| \|----\|-------\| \|Black and Gold Autonomous Racing\|B\| \|TUM Autonomous Motorsport\|T\| \|KAIST\|K\| \|PoliMOVE\|P\| \|TII EuroRacing\|E\| \|AI Racing Tech\|H\| \|MIT-PITT-RW\|M\| \|Cavalier Autonomous Racing\|C\| \|Autonomous Tiger Racing\|A\| ## Data Usage and Availability ### Data Usage and License This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0). To obtain a copy of this license, see LICENSE-CC-BY-NC-4.0.txt in the archive, visit CreativeCommons.org or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. ### Citation Please refer to our [paper](https://arxiv.org/abs/2306.03252) for more information and cite it if you use it in your research. ``` @conference{racecar2023, title={RACECAR - The Dataset for High-Speed Autonomous Racing}, author={Amar Kulkarni and John Chrosniak and Emory Ducote and Florian Sauerbeck and Andrew Saba and Utkarsh Chirimar and John Link and Marcello Cellina and Madhur Behl}, year={2023}, month={October}, booktitle={International Conference on Intelligent Robots and Systems (IROS)}, publisher={IEEE/RSJ} } ``` ### Availability #### AWS S3 Bucket Both the ROS2 and nuScenes datasets are available on [AWS S3](https://aws.amazon.com/s3/). - AWS Bucket Name: s3://racecar-dataset - Region: us-west-2 The bucket is organized by (RACECAR-ROS2) 1. Dataset Format (`RACECAR-ROS2` or `RACECAR-nuScenes`) 2. Scenario (`S1`, `S2`,...,`S11`) 3. Scene ('M-MULTI-SLOW_KAIST', 'E-SOLO-FAST-100-140', etc.) (RACECAR-nuScenes) 1. Dataset Format (`RACECAR-ROS2` or `RACECAR-nuScenes`) 2. Category ('MULTI-FAST', 'MULTI-SLOW',etc) Download using AWS Command Line Interface (Recommended) Multiple objects or folders can be downloaded using the AWS CLI. See these instructions for [installing AWS CLI v2](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html). Example download usage: ``` aws s3 cp s3://racecar-dataset/RACECAR-ROS2/S5/M-MULTI-SLOW-KAIST . --recursive --no-sign-request ``` This command will download the corresponding rosbag2 folder containing the metadata and db3 file. Download using URL Only individual objects can be downloaded using URLs, making them inconvenient for downloading rosbags. Example URL: * https://racecar-dataset/RACECAR-nuScenes/metadata.tar ## Data Organization The dataset is released in both the <a href="https://github.com/ros2/rosbag2" target="_blank">rosbag2</a> and nuScenes format. Under the dataset root directory, two folders seperate the [ROS2](#folder-structure) and [nuScenes](#folder-structure-1) directories. ``` ├── data │ ├── RACECAR nuScenes │ ├── RACECAR ``` ## Scenario Description Each recorded autonomous run is classified by a scenario description. This indicates the speed range of the run, the track the run takes place, and whether or not the run is multi-agent. Also specified are which teams contributed to each scenario. \|Scenario\|Track\|Description\|Speeds\|Teams\| \|----------\|----------\|-----------\|-----------\|-------\| \|S<sub>1</sub>\|LVMS\|Solo Slow Lap\|\< 70 mph\|C, M, P\| \|S<sub>2</sub>\|LVMS\|Solo Slow Lap\|70-100 mph\|C,M\| \|S<sub>3</sub>\|LVMS\|Solo Fast Lap\|100-140 mph\|E,M\| \|S<sub>4</sub>\|LVMS\|Solo Fast Lap\|\> 140 mph\|E,T\| \|S<sub>5</sub>\|LVMS\|Multi-Agent Slow\|\< 100 mph\|C,E,K,M,P,T\| \|S<sub>6</sub>\|LVMS\|Multi-Agent Fast\|\> 130 mph\|E,T\| \|S<sub>7</sub>\|IMS\|Solo Slow Lap\|\< 70 mph\|C\| \|S<sub>8</sub>\|IMS\|Solo Slow Lap\|70-100 mph\|\| \|S<sub>9</sub>\|IMS\|Solo Fast Lap\|100-140 mph\|E,T\| \|S<sub>10</sub>\|IMS\|Solo Fast Lap\|\> 140 mph\|P\| \|S<sub>11</sub>\|IMS\|Pylon Avoidance\|\< 70 mph\|T\| \ C - Cavalier, E - EuroRacing, K - KAIST, M - MIT-PITT-RW, P - PoliMove, T - TUM ## Coordinate Conventions The novatel pwrpak7 used to collect GNSS measurements on the AV21 uses a Y-forward, X-right, Z-up coordinate convention. Exact measurements and orientation can be found [here](https://docs.novatel.com/OEM7/Content/Technical_Specs_Receiver/PwrPak7_Mechanicals.htm). Due to cabling considerations, the placement of the unit is rotated 180 degrees around the Z-axis in the vehicle. Therefore orientation measurements coming from topics such as `novatel_oem7_msgs/msg/BESTVEL`, must be rotated 180 degrees in order to correctly correspond to the YXZ convention. The accompanying Unified Robotics Description Format (urdf) has a coordinate convention of X-forward, Y-left, Z-up. In order to properly match with this convention, orientation and velocity measurements (from the IMU for example) should be rotated a subsequent 90 degrees counter-clockwise. A 90 degree clockwise rotation will equate to the same series of transformations. ![](docs/images/coordinate_frames.png) We have taken into account these rotations in the `local_odometry` topic, but if you desire to use the raw measurements to do your own sensor fusion or filtering, please take these orientations into account. The accompanying urdf, located in `racecar_utils/urdf` contains joints for every sensor on the car, as well as the approximate center of gravity. These were measured during the initial assembly of the car. ![](docs/images/av21_urdf.png) ## RACECAR ROS2 - Data Structure ### Folder Structure ``` RACECAR ├── S1 │ ├── C_SOLO-SLOW-70 │ │ ├── metadata.yaml │ │ └── SOLO-SLOW-70.db3 │ ├── M_SOLO-SLOW-70 │ │ ├── metadata.yaml │ │ └── SOLO-SLOW-70.db3 │ └── P_SOLO-SLOW-70 │ ├── metadata.yaml │ └── SOLO-SLOW-70.db3 ... ├── S6 │ ├── E_MULTI-FAST-TUM │ │ ├── metadata.yaml │ │ └── MULTI-FAST-TUM.db3 │ ├── T_MULTI-FAST-EURO │ │ ├── metadata.yaml │ │ └── MULTI-FAST-EURO.db3 │ └── T_MULTI-FAST-POLI │ ├── metadata.yaml │ └── MULTI-FAST-POLI.db3 ``` The ROS2 folder structure is organized by scenario, with each scenario folder containing a collection of rosbags. The rosbags are named corresponding to contributing racing team and a short scenario description. Inside the rosbag are the typical metadata and sqlite files. ``` TEAM_DESCRIPTION ``` ### Data Processing The ROS2 data was parsed and processed using python utility scripts located in `racecar_py`. You can use these [scripts](racecar_py) as a baseline for doing your own bag conversion or coordiante system transformations. ### Topic List All topics in the dataset are namespaced by their vehicle number. Each rosbag contains all sensor data available from the ego vehicle, and if a multi-agent label is included, it will be present as an `nav_msgs/msg/Odometry` topic named `local_odometry`. If additional namespacing or merging is required, a script is included in the racecar_utils folder called `rosbag_merger`. The inbuilt rosbag2 cli tools available in ROS2 Humble are also useful for performing bag merging and conversion. \|Topic Name\|Topic Type\|Description\| \|----------\|----------\|-----------\| Camera Topics \|`camera/xxx/camera_info`\| `sensor_msgs/msg/CameraInfo`\|Distortion parameters and Intrinsic Camera Matrix\| \|`camera/xxx/image/compressed`\| `sensor_msgs/msg/CompressedImage`\|Compressed camera image buffer and compression format\| LIDAR Topics \|`luminar_points`\| `sensor_msgs/msg/PointCloud2`\|Merge LiDAR point cloud from all three sensors\| \|`luminar_xxx_points`\| `sensor_msgs/msg/PointCloud2`\|LiDAR point cloud corresponding to xxx sensor\| GNSS Topics \|`novatel_xxx/bestgnsspos`\| `novatel_oem7_msgs/msg/BESTPOS`\|Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\| \|`novatel_xxx/bestpos`\| `novatel_oem7_msgs/msg/BESTPOS`\|Best available GNSS solution from Novatel PwrPak. Measurement located at antenna phase center transmitted at 20 Hz\| \|`novatel_xxx/bestvel`\| `novatel_oem7_msgs/msg/BESTVEL`\|Velocity derived from differentiated position. Uses the same solution method from BESTPOS transmitted at 20 Hz\| \|`novatel_xxx/heading2`\| `novatel_oem7_msgs/msg/HEADING2`\|Heading derived from alignment of dual antenna system at variable rate\| \|`novatel_xxx/oem7raw`\| `novatel_oem7_msgs/msg/Oem7RawMsg`\|Binary data received from Novatel receivers before driver processing\| \|`novatel_xxx/rawimu`\| `novatel_oem7_msgs/msg/RAWIMU`\|Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\| \|`novatel_xxx/rawimux`\| `sensor_msgs/msg/Imu`\|Accelerometer and Gyroscope data transmitted from receiver at 125 Hz\| \|`novatel_xxx/time`\| `novatel_oem7_msgs/msg/TIME`\|Satellite time accompanying GNSS packets\| Radar Topics \|`radar_front/esr_status1`\| `delphi_esr_msgs/msg/EsrStatus1`\|\| \|`radar_front/esr_status2`\| `delphi_esr_msgs/msg/EsrStatus2`\|\| \|`radar_front/esr_status3`\| `delphi_esr_msgs/msg/EsrStatus3`\|\| \|`radar_front/esr_status4`\| `delphi_esr_msgs/msg/EsrStatus4`\|\| \|`radar_front/esr_status5`\| `delphi_esr_msgs/msg/EsrStatus5`\|\| \|`radar_front/esr_status6`\| `delphi_esr_msgs/msg/EsrStatus6`\|\| \|`radar_front/esr_status7`\| `delphi_esr_msgs/msg/EsrStatus7`\|\| \|`radar_front/esr_status8`\| `delphi_esr_msgs/msg/EsrStatus8`\|\| \|`radar_front/esr_track`\| `delphi_esr_msgs/msg/EsrTrack`\|Radar detection\| \|`radar_front/esr_valid1`\| `delphi_esr_msgs/msg/EsrValid1`\|\| \|`radar_front/esr_valid2`\| `delphi_esr_msgs/msg/EsrValid2`\|\| \|`radar_front/esr_vehicle1`\| `delphi_esr_msgs/msg/EsrVehicle1`\|\| \|`radar_front/esr_vehicle2`\| `delphi_esr_msgs/msg/EsrVehicle2`\|\| \|`radar_front/from_can_bus`\| `can_msgs/msg/Frame`\|Raw CAN data received from Aptiv ESR Radar\| \|`radar_front/to_can_bus`\| `can_msgs/msg/Frame`\|Raw CAN data sent to Aptiv ESR Radar\| \|`radar_front/radar_visz_moving`\| `visualization_msgs/msg/Marker`\|Visualization of radar detection\| \|`radar_front/radar_visz_static`\| `visualization_msgs/msg/Marker`\|Visualization of radar detection\| \|`radar_xxx/marker`\| `visualization_msgs/msg/Marker`\|Visualization of radar detection\| \|`radar_xxx/detection`\| `delphi_mrr_msgs/msg/Detection`\|Detection from Aptiv MRR Radar\| Vehicle Positions \|`local_odometry`\| `nav_msgs/msg/Odometry`\|Vehicle odometry in Cartesian coordinates derived from RTK GNSS solution\| Topic placeholders `xxx` refer to the specific sensor. For the cameras there is: - `front_left` - `front_right` - `front_left_center` - `front_right_center` - `rear_left` - `rear_right` For LIDAR: - `luminar_front_points` - `luminar_left_points` - `luminar_right_points` For GNSS: - `novatel_top` - `novatel_bottom` ## RACECAR nuScenes - Data Structure We have also released the dataset in the [nuScenes format](https://www.nuscenes.org/nuscenes) for easier accessibility to those unfamiliar with ROS2. The conversion process is done using the [rosbag2nuscenes](https://github.com/linklab-uva/rosbag2nuscenes/tree/main) conversion tool. ![nuScenes Block Diagram](https://www.nuscenes.org/public/images/nuscenes-schema.svg) ### Folder Structure The nuScenes dataset is structured as follows: ``` RACECAR nuScenes ├── v1.0-mini │ ├── scene.json │ ├── log.json │ ├── map.json │ ├── sample.json │ ├── sample_data.json │ ├── ego_pose.json │ ├── calibrated_sensor.json │ ├── sensor.json │ ├── instance.json │ ├── sample_annotation.json │ ├── category.json │ ├── attribute.json │ └── visibility.json ├── samples │ ├── SENSOR1 │ │ ├── data(.png, .pcd, .pcd.bin) │ │ └── ... │ └── SENSOR2 │ ├── data(.png, .pcd, .pcd.bin) │ └── ... ├── sweeps │ ├── SENSOR1 │ │ ├── data(.png, .pcd, .pcd.bin) │ │ └── ... │ └── SENSOR2 │ ├── data(.png, .pcd, .pcd.bin) │ └── ... ``` For more information on the contents of each JSON file, please refer to [the nuScenes documentation](https://www.nuscenes.org/nuscenes#data-format). Our nuScenes schema deviates slightly from the original. First, we have classified each ROS2 bag as a scene rather than splitting each bag into twenty second intervals. We believe the longer scene intervals (typically over 10 mins) widen opportunities for exploration into mapping and localization problems. Second, our dataset has no entries in the Annotation or Taxonomy JSON files due to the absence of annotations. These files are still present but have dummy entires to maintain compatibilty with the [Python nuScenes development kit](https://pypi.org/project/nuscenes-devkit/). Each scene in this format is seperated by the same [Scenario](#scenario-description) classification as the rosbags. [This guide](TODO) provides a walkthrough of how to explore the nuScenes release using the Python development kit. Similar to the nuScenes release, we have batched the sensor data from each scene into separate tarballs to allow users to only download the data they are interested in working with. Each tarball follows the naming convention of `{TEAM_NAME}_{BAG NAME}.tar.gz`. ## Tutorials ### Tutorial 1: ROS2 Visualization #### Requirements All code was tested with the following environment. - Linux (tested on Ubuntu 20.04/22.04) - Python 3.8+ For `racecar_utils` please install the following. - ROS2 (<a href="https://docs.ros.org/en/galactic/Installation.html" target="_blank">Galactic</a>/<a href="https://docs.ros.org/en/humble/Installation.html" target="_blank">Humble</a>) - <a href="https://eigen.tuxfamily.org/index.php?title=Main_Page" target="_blank">Eigen3</a> #### Installation of Custom ROS2 Messages The `delphi_esr_msgs`, `novatel_oem7_msgs`, and `novatel_gps_msgs` are the radar and gps messages obtained from the Autonomous Stuff and Novatel drivers. Install these packages in order to parse the radar and novatel custom messages in the dataset. They are all located in the `ros2_custom_msgs` directory. - novatel_oem7_msgs - novatel_gps_msgs - delphi_esr_msgs - can_msgs First create a dev workspace that looks like the following. This will be our working directory. ``` ├── data │ └── RACECAR └── racecar_ws ├── conversion_configs ├── urdf ├── racecar_py ├── racecar_utils ├── ros2_custom_msgs └── rosbag2nuscenes ``` In the working directory source your ROS2 installation, and build the packages in `racecar_utils` and `ros2_custom_msgs`. Source the installation folder in the working directory to then use the installed messages. ``` source /opt/ros/${ROS_DISTRO}/setup.bash colcon build source install/setup.bash ``` The `can_msgs` package should be available via apt. ``` sudo apt install ros-${ROS_DISTRO}-can-msgs ``` #### Visualization in RVIZ When replaying a bag, it is recommended to publish the ego vehicles odometry as part of the tf tree, in order to visualize it's position and sensor data in reference to the inertial map frame. We have provided an example node `odom_to_tf.cpp`, that takes in the `local_odometry` topics from both the ego and opponenet vehicles and publishes them to the tf tree. It is important to have the ego vehicle's frame match up with the appropriate frame in the URDF so that LiDAR and Radar point clouds can be easily visualized. The node and accompanying launch file should be built along with `racecar_utils`. To run, use the launch file and use the provided parameters to remap the odometry topic names appropriately. Click the following image to see an example of the RVIZ visualization. ``` ros2 launch racecar_utils odom_to_tf.launch.py ego_topic:=/vehicle_3/local_odometry opp_topic:=/vehicle_5/local_odometry ``` [![RVIZ LiDAR Viz](http://img.youtube.com/vi/5KDiXADwiO8/0.jpg)](http://www.youtube.com/watch?v=5KDiXADwiO8 "RACECAR Dataset - GPS Labels of LiDAR") Camera RVIZ We have also provided an RVIZ config for visualizing camera images from the bags. ``` ./racecar_utils/rviz/cameras.rviz ``` ![Camera RVIZ Example](docs/images/camera_rviz.png) Please note that this RVIZ configuration is set to show the images from all six bag topics in the format `camera_idX_imageU8`, which is different from the specified camera topics above. If you would like to visualize other camera topics, you may simply change the topic information in the RVIZ configuration. ### Tutorial 2: ROS2 Localization An example of using the dataset is creating a more robust localization method than just using GPS. If you have examined a few of the scenarios, you may notice that there are occasional message drops, spikes in GNSS standard deviation, or small abrubt shifts in reported position. For accurate object detection, having smooth unfettered orientation estimates is very useful, so we will implement a simple extended kalman filter in order to filter through these noisy measurements. An open source package, `robot_localization` which is shipped as part of the full ROS2 installation will suffice to fuse measurements from a GNSS receiver, and an IMU. Install the package with the following command. For additional details about using the package, please reference the [repository documentation](https://github.com/cra-ros-pkg/robot_localization/blob/ros2/doc/configuring_robot_localization.rst) directly. ``` sudo apt install ros-${ROS_DISTRO}-robot-localization ``` In order to use the extended kalman filter, we must transform our inputs into standard message types, and make sure they are in a common coordinate system. Please see [Coordinate Conventions](#coordinate-conventions) for the required rotations. Using the `convert_imu` node, we convert the `novatel_oem7_msgs/msg/RAWIMU` message to the standard `sensor_msgs/msg/Imu` which feeds into `robot_localization`. The `local_odometry` topic is already a stantard message type, and does not need to be adjusted. We provide a simple configuration file `config/ekf.yaml` which instructs the ekf node to subscribe to the `local_odometry` topic, and the frame corrected IMU topics. To run the example, source the workspace and run your selected bag using a clock message. Subsequently run the provided launch file ``` ros2 bag play YOUR/BAG/HERE --clock 100.0 ``` ``` ros2 launch racecar_utils localization.launch.py ns:=vehicle_x use_sim_time:=true ``` Using a different motion model, tweaking the sensor measurement covariances, and adjusting which inputs are used, are all methods to gain more stable performance from the filter. ### Tutorial 3: nuScenes jupyter notebook For a full walkthrough of using the nuScenes devkit and loading and visualizing the RACECAR data using it, please refer to this [jupyter notebook](nuscenes_tutorial.ipynb). ## Acknowledgement The RACECAR data would not be possible without the efforts and contributions of the following individuals. Amar Kulkarni, John Chrosniak, Emory Ducote, Utkarsh Chirimar, John Link, Madhur Behl, Andrew Shehab Saha, Calvin Chanyoung Jung, Andrea Tecozzi, Marcello Cellina, Giulio Panzani, Matteo Corno, Phillip Karle, Florian Sauerbeck, Sebastian Huch, Maximilian Geisslinger, Felix Fent, Micaela Verucchi, Ayoub Raji, Danilo Caporale, Francesco Gatti.
suanlixianren/voc114514	2023-08-24T01:18:26.000Z	[ "region:us" ]	suanlixianren	null	null	null	0	Entry not found
griffin/dense_summ	2023-08-29T19:14:08.000Z	[ "region:us" ]	griffin	null	null	null	1	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: completion dtype: string - name: step dtype: string splits: - name: train num_bytes: 3878873 num_examples: 798 download_size: 1757801 dataset_size: 3878873 --- # Dataset Card for "dense_summ" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amitness/logits-ar-kmt-512	2023-08-24T03:33:17.000Z	[ "region:us" ]	amitness	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 26479205248.58042 num_examples: 1698296 - name: test num_bytes: 4672811932.077537 num_examples: 299700 download_size: 10941745120 dataset_size: 31152017180.65796 --- # Dataset Card for "logits-ar-kmt-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kingsley9/patientNarratives	2023-08-24T02:10:02.000Z	[ "region:us" ]	kingsley9	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: description dtype: string - name: medical_specialty dtype: string - name: sample_name dtype: string - name: narrative dtype: string - name: keywords dtype: string - name: narrative_length dtype: int64 splits: - name: train num_bytes: 1547287.108 num_examples: 439 - name: validation num_bytes: 172704.028 num_examples: 49 download_size: 943968 dataset_size: 1719991.136 --- # Dataset Card for "patientNarratives" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NarchAI1992/Neo_classic_house	2023-08-24T02:16:26.000Z	[ "license:openrail", "region:us" ]	NarchAI1992	null	null	null	0	--- license: openrail ---
thisisHJLee/rlhf_sft_B3	2023-08-24T02:19:29.000Z	[ "region:us" ]	thisisHJLee	null	null	null	0	Entry not found
thisisHJLee/rlhf_sft_A2	2023-08-24T03:26:58.000Z	[ "region:us" ]	thisisHJLee	null	null	null	0	Entry not found
IanZZZ/red_plus	2023-08-24T03:22:11.000Z	[ "region:us" ]	IanZZZ	null	null	null	0	Entry not found
soulteary/warm-chicken-soup	2023-08-24T03:35:48.000Z	[ "license:other", "region:us" ]	soulteary	null	null	null	0	--- license: other ---
NexaAI/Accessories	2023-09-05T03:05:25.000Z	[ "region:us" ]	NexaAI	null	null	null	0	Entry not found
coconutzhang/ghc_session_data_v1	2023-08-28T17:59:52.000Z	[ "region:us" ]	coconutzhang	null	null	null	0	--- dataset_info: features: - name: User dtype: string - name: Prompt dtype: string splits: - name: train num_bytes: 76200 num_examples: 289 download_size: 23679 dataset_size: 76200 --- # Dataset Card for "ghc_session_data_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
eunyounglee/rm_data_added	2023-08-24T05:03:07.000Z	[ "region:us" ]	eunyounglee	null	null	null	0	Entry not found
goniyuni/nva-scd	2023-08-24T05:11:14.000Z	[ "region:us" ]	goniyuni	null	null	null	0	Entry not found
nuprl/stack_dedup_lua_codegen_full	2023-08-24T05:18:55.000Z	[ "region:us" ]	nuprl	null	null	null	0	--- dataset_info: features: - name: content dtype: string - name: pass_rate dtype: float64 - name: id dtype: int64 - name: original_id dtype: int64 - name: tests dtype: string - name: edu_score dtype: float64 splits: - name: train num_bytes: 152206357 num_examples: 117557 download_size: 51503174 dataset_size: 152206357 --- # Dataset Card for "stack_dedup_lua_codegen_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NexaAI/Blouse	2023-09-13T06:34:56.000Z	[ "region:us" ]	NexaAI	null	null	null	0	Entry not found
NexaAIDev/Coat	2023-08-30T09:58:13.000Z	[ "region:us" ]	NexaAIDev	null	null	null	0	Entry not found
NexaAI/Overcoat	2023-09-13T04:40:25.000Z	[ "region:us" ]	NexaAI	null	null	null	0	Entry not found
NexaAIDev/Jeans	2023-08-30T03:34:00.000Z	[ "region:us" ]	NexaAIDev	null	null	null	0	Entry not found
NexaAI/Plus	2023-08-31T08:52:53.000Z	[ "region:us" ]	NexaAI	null	null	null	0	Entry not found
NexaAI/Shirt	2023-08-24T06:14:09.000Z	[ "region:us" ]	NexaAI	null	null	null	0	Entry not found
NexaAIDev/Trousers	2023-09-01T03:36:12.000Z	[ "region:us" ]	NexaAIDev	null	null	null	0	Entry not found
AIBluefihser/test	2023-08-24T05:27:27.000Z	[ "region:us" ]	AIBluefihser	null	null	null	0	Entry not found
trajanson/ralph-lauren-purple-label-polo-images	2023-08-24T06:11:08.000Z	[ "region:us" ]	trajanson	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 15882713.0 num_examples: 117 download_size: 15861077 dataset_size: 15882713.0 --- # Dataset Card for "ralph-lauren-purple-label-polo-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Yukang/pile-subset2	2023-08-24T05:40:11.000Z	[ "region:us" ]	Yukang	null	null	null	0	Entry not found
alex1qaz/test	2023-08-24T05:41:17.000Z	[ "license:openrail", "region:us" ]	alex1qaz	null	null	null	0	--- license: openrail ---
HGV1408/pegasus_samsum	2023-08-24T05:59:30.000Z	[ "generated_from_trainer", "region:us" ]	HGV1408	null	null	null	0	--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6997 \| 0.54 \| 500 \| 1.4834 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
KamranHussain05/Summarization	2023-08-24T06:29:40.000Z	[ "license:cc", "region:us" ]	KamranHussain05	null	null	null	0	--- license: cc ---
Nehu/DEMO	2023-08-24T06:00:04.000Z	[ "region:us" ]	Nehu	null	null	null	0	Entry not found
junhyekh/col-with-empty	2023-08-24T08:16:39.000Z	[ "region:us" ]	junhyekh	null	null	null	0	Entry not found
eunyounglee/squad_custom	2023-08-24T06:16:47.000Z	[ "region:us" ]	eunyounglee	null	null	null	0	Entry not found
kla-20/sai-literature-doc-embeddings	2023-08-24T06:13:37.000Z	[ "license:apache-2.0", "region:us" ]	kla-20	null	null	null	0	--- license: apache-2.0 ---
emmanueldave/nusa_t2t	2023-08-24T06:20:46.000Z	[ "region:us" ]	emmanueldave	null	null	null	0	Entry not found
dongyoung4091/shp-generated_flan_t5_rx_xl	2023-08-24T06:20:42.000Z	[ "region:us" ]	dongyoung4091	null	null	null	0	--- dataset_info: features: - name: response dtype: string - name: prompt dtype: string - name: reward_score dtype: float64 - name: __index_level_0__ dtype: string splits: - name: train num_bytes: 27255555 num_examples: 25600 download_size: 2031444 dataset_size: 27255555 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "shp-generated_flan_t5_rx_xl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dongyoung4091/shp-generated_flan_t5_rx_xl_all	2023-08-24T06:23:20.000Z	[ "region:us" ]	dongyoung4091	null	null	null	0	--- dataset_info: features: - name: response dtype: string - name: prompt dtype: string - name: model_A dtype: float64 - name: model_B dtype: float64 - name: __index_level_0__ dtype: string splits: - name: train num_bytes: 27460355 num_examples: 25600 download_size: 2234625 dataset_size: 27460355 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "shp-generated_flan_t5_rx_xl_all" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alex1qaz/conll2003	2023-08-24T06:35:02.000Z	[ "license:openrail", "region:us" ]	alex1qaz	The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2 tagging scheme, whereas the original dataset uses IOB1. For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419	@inproceedings{tjong-kim-sang-de-meulder-2003-introduction, title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", author = "Tjong Kim Sang, Erik F. and De Meulder, Fien", booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", year = "2003", url = "https://www.aclweb.org/anthology/W03-0419", pages = "142--147", }	null	0	--- license: openrail ---
frank-chieng/word2vector	2023-08-24T06:33:16.000Z	[ "region:us" ]	frank-chieng	null	null	null	0	Entry not found
gwj/nuoaier	2023-08-24T06:38:41.000Z	[ "region:us" ]	gwj	null	null	null	0	Entry not found
HuangHaoyang/test1	2023-08-24T06:52:06.000Z	[ "region:us" ]	HuangHaoyang	null	null	null	0	--- dataset_info: features: - name: image dtype: image - name: test1 dtype: image splits: - name: train num_bytes: 441047.0 num_examples: 2 download_size: 440372 dataset_size: 441047.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
neil-code/autotrain-data-tabular-data-classification	2023-08-24T06:43:36.000Z	[ "region:us" ]	neil-code	null	null	null	0	Entry not found
neil-code/autotrain-data-summarization	2023-08-24T07:12:45.000Z	[ "task_categories:summarization", "language:en", "region:us" ]	neil-code	null	null	null	0	--- language: - en task_categories: - summarization --- # AutoTrain Dataset for project: summarization ## Dataset Description This dataset has been automatically processed by AutoTrain for project summarization. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_id": "train_0", "text": "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", "target": "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", "feat_topic": "get a check-up" }, { "feat_id": "train_1", "text": "#Person1#: Hello Mrs. Parker, how have you been?\n#Person2#: Hello Dr. Peters. Just fine thank you. Ricky and I are here for his vaccines.\n#Person1#: Very well. Let's see, according to his vaccination record, Ricky has received his Polio, Tetanus and Hepatitis B shots. He is 14 months old, so he is due for Hepatitis A, Chickenpox and Measles shots.\n#Person2#: What about Rubella and Mumps?\n#Person1#: Well, I can only give him these for now, and after a couple of weeks I can administer the rest.\n#Person2#: OK, great. Doctor, I think I also may need a Tetanus booster. Last time I got it was maybe fifteen years ago!\n#Person1#: We will check our records and I'll have the nurse administer and the booster as well. Now, please hold Ricky's arm tight, this may sting a little.", "target": "Mrs Parker takes Ricky for his vaccines. Dr. Peters checks the record and then gives Ricky a vaccine.", "feat_topic": "vaccines" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_id": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)", "feat_topic": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 1999 \| \| valid \| 499 \|
St4n/my_datasets	2023-08-24T09:22:34.000Z	[ "language:en", "license:unknown", "region:us" ]	St4n	Self-use DataSets	@article{nothing, title={Self-use DataSets}, author={Stan} journal={}, year={2023} }	null	0	--- license: unknown language: - en ---
TaylorAI/finetuning-mix	2023-08-24T07:03:04.000Z	[ "region:us" ]	TaylorAI	null	null	null	0	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 318765311 num_examples: 364298 download_size: 195260468 dataset_size: 318765311 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "finetuning-mix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lakshya1999/esner	2023-08-24T08:23:00.000Z	[ "task_categories:token-classification", "size_categories:10K<n<100K", "language:en", "legal", "region:us" ]	lakshya1999	null	null	null	0	--- task_categories: - token-classification language: - en tags: - legal size_categories: - 10K<n<100K ---
Imran1/Svit	2023-08-24T07:11:10.000Z	[ "region:us" ]	Imran1	null	null	null	0	Entry not found
roszcz/tmp-midi-clip	2023-08-24T07:24:53.000Z	[ "region:us" ]	roszcz	null	null	null	0	--- dataset_info: features: - name: midi_filename dtype: string - name: pitch sequence: int16 length: 32 - name: dstart_bin sequence: int16 length: 32 - name: duration_bin sequence: int16 length: 32 - name: velocity_bin sequence: int16 length: 32 splits: - name: train num_bytes: 118752197 num_examples: 352232 - name: validation num_bytes: 13434506 num_examples: 39754 - name: test num_bytes: 15540656 num_examples: 46073 download_size: 21481498 dataset_size: 147727359 --- # Dataset Card for "tmp-midi-clip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ismansanik/merdeka	2023-08-24T08:39:41.000Z	[ "region:us" ]	ismansanik	null	null	null	0	Entry not found
qgyd2021/wechat_or_qq_icon_detection	2023-08-24T09:18:11.000Z	[ "size_categories:100K<n<1M", "language:zh", "art", "region:us" ]	qgyd2021	null	@dataset{early_media, author = {Xing Tian}, title = {wechat_or_qq_icon_detection}, month = aug, year = 2023, publisher = {Xing Tian}, version = {1.0}, }	null	1	--- language: - zh tags: - art size_categories: - 100K<n<1M --- ## WeChat 或 QQ 图标检测. 任务: 从图像中检测出 WeChat 或 QQ 图标的位置. 方法: 基于 OpenCV 库, 通过 SIFT 或 SURF 图像特征作检测. ```text 由于 SURF 和 SIFT 算法有专利限制, 其安装环境是: python==3.6.5 opencv-contrib-python==3.4.2.16 由于 huggingface 的 space 中的 python 为 3.10 版本, 已无法安装此 opencv 库. 你也可以在这里找到相关代码, 但这个库已经不再维护了. https://github.com/tianxing1994/OpenCV/ dir: 练习实例 -> SIFT_SURF 图像特征作目标检测(在图片中检测出 QQ 图标的位置) 因此将实现方法记录如下: ``` 实现步骤: (1)采用 opencv 库的 SIFT 或 SURF 图像特征 `cv.xfeatures2d.SIFT_create()` 对目标区域生成特征点向量. (2)为减少特征点数量, 对所有的特征点向量做聚类, 将聚类中心作为特征点, 得到 `template_descriptors`. (3)计算目标图像中的所有 SIFT 或 SURF 图像特征点. 得到 `scene_descriptors`. (4) 采用 `flann.knnMatch` 方法进行特征点匹配, (如果最近匹配距离比第二匹配距离的 0.7 倍还要小, 则认为这个点是 `good_match`, 即认为好的特征点具有独特征, 不会有其它更相似的点). (5)最小要有3个 `good_match` 才认为图标匹配成功. (6)最后根据 `good_match` 点的分布计算图标的中心, 我的做法是计算点集中心和平均距离, 将大于平均距离的点丢弃, 重复此过程, 直到半径小于阈值. 涉及到的方法有: ```python import cv2 as cv sift = cv.xfeatures2d.SIFT_create() index_params = dict(algorithm=0, trees=5) search_params = dict(checks=50) flann = cv.FlannBasedMatcher(index_params, search_params) keypoints, descriptors = sift.detectAndCompute(image, None) matches = flann.knnMatch( queryDescriptors=template_descriptors, trainDescriptors=scene_descriptors, k=2 ) good_matches = [] for m, n in matches: if m.distance < 0.7 * n.distance: good_matches.append(m) k = 3 if len(good_matches) > k: print(good_matches) else: print("Not enough matches are found - %d/%d" % (len(good_matches), k)) ``` 参考链接: ```text https://docs.opencv.org/3.0-beta/index.html ``` bbox 对应类别 category 的文字为: ```text CATEGORIES = [ "红黑白QQ图标", "绿边白色微信图标", "淡蓝底全白QQ图标", "绿底全白微信图标", "大绿小白微信图标", "纯色微信图标", "纯色QQ图标" ] ```
gurprbebo/Bebo_V1	2023-08-24T07:50:04.000Z	[ "region:us" ]	gurprbebo	null	null	null	0	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 833 num_examples: 6 download_size: 1803 dataset_size: 833 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Bebo_V1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Madhubabu/madhubabu	2023-08-24T07:58:34.000Z	[ "license:openrail", "region:us" ]	Madhubabu	null	null	null	0	--- license: openrail ---
Gary3410/object_tmp	2023-09-14T01:59:22.000Z	[ "region:us" ]	Gary3410	null	null	null	0	Entry not found
rathoddeepak/my	2023-08-24T08:07:47.000Z	[ "region:us" ]	rathoddeepak	null	null	null	0	Entry not found
narae3759/TrafficAccident	2023-08-24T08:34:24.000Z	[ "region:us" ]	narae3759	null	null	null	0	Entry not found
bpluskapseln/Bplus	2023-08-24T08:41:11.000Z	[ "region:us" ]	bpluskapseln	null	null	null	0	Hier ist das beste Keto-Shake-Kraftstoffergänzungsmittel von Amazon Choice mit über 500 positiven Rückmeldungen. MCT-Ölprodukte sind zu beliebten Kraftstoffquellen für ketogene Praktiker geworden. „Ernährungsketose“ bezieht sich auf die Programmierung der Ketose durch den Verzehr von Fetten und die Eliminierung von Kohlenhydraten. B+ Kapseln Erfahrungen Bplus test stiftung warentest Bplus Kapseln höhle der löwen Body+ Kapseln B+ Weight Management Kapseln Erfahrungen Das ist keine Garantie, aber Sie können mit Sicherheit sagen: Wenn ich keine Symptome habe, habe ich im Allgemeinen kein Problem mit SIBO oder anderen damit verbundenen Ungleichgewichten wie Dysbiose. Wenn das der Fall wäre, gibt es noch andere Dinge, die Sie sich ansehen könnten. Ich weiß nicht, ob er sagt, dass seine Labortests normal sind, er aber immer noch müde ist, oder ob sich seine Darmsymptome verbessert haben und er immer noch müde ist. Denn es kann sein, dass es immer noch ein Problem im Darm gibt, nur wissen wir aufgrund der etwas vagen Terminologie nicht genau, was das ist. https://www.supplementz.org/b-kapseln-erfahrungen/ https://www.supplementz.org/b-weight-management-avis/ https://www.supplementz.org/peoples-keto-gummies/ https://www.supplementz.org/slimming-gummies-erfahrungen/ https://www.supplementz.org/slimming-gummies-avis/ https://www.supplementz.org/slimming-gummies-ervaringen/ https://www.supplementz.org/slimming-gummies-erfaring/ https://www.supplementz.org/ketoxplode-gummies-erfahrungen/ https://www.supplementz.org/ketoxplode-gummies-avis/ https://www.supplementz.org/keto-xplode-biverkningar/ https://www.supplementz.org/ivitasana-weight-loss-formula-pro-nl/ https://www.supplementz.org/ketoxboom-erfahrungen/
danadesa/disporaAa	2023-08-24T08:47:44.000Z	[ "region:us" ]	danadesa	null	null	null	0	Entry not found
hao7Chen/test_dataset	2023-08-24T08:52:16.000Z	[ "license:mit", "region:us" ]	hao7Chen	null	null	null	0	--- license: mit ---
bekinganepusat/gusSs	2023-08-24T09:14:14.000Z	[ "region:us" ]	bekinganepusat	null	null	null	0	Entry not found
bogeumkim/emotion_cls	2023-08-24T09:13:33.000Z	[ "region:us" ]	bogeumkim	null	null	null	0	Entry not found
ktwszFingoweb/question_classification_pl_2	2023-08-24T09:16:17.000Z	[ "region:us" ]	ktwszFingoweb	null	null	null	0	Entry not found
pressure-aid/peak-biome-pressure-aid	2023-08-24T09:15:27.000Z	[ "region:us" ]	pressure-aid	null	null	null	0	✔️ Product Name - [Pressure Aid](https://peak-biome-pressure-aid-1.jimdosite.com/) ✔️ Category - Blood Pressure Support Formula ✔️ Compostion - Natural Ingredients Only ✔️ Results - In 1-2 Months ✔️ Availability – [Online](https://www.healthsupplement24x7.com/get-pressure-aid) ✔️ Rating: - 4.8/5.0 ★★★★☆ ✔️ Official Website - [https://www.healthsupplement24x7.com/get-pressure-aid](https://www.healthsupplement24x7.com/get-pressure-aid) [Pressure Aid](https://healthsupplements24x7.blogspot.com/2023/08/pressure-aid.html) uses all-powerful natural ingredients which are good for the health of the heart and blood. It defends the body against oxidative stress. It also increases the level of nitric oxide, which regulates healthy blood flow in the body. It helps the nervous system in the body relax and boosts the immune system. Consequently improving the overall health of the body. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiwN912ITlBh7QwOLLwgLTvabZ6pfv5cnOllEOkOVvXaHDpNOEBdec66Na535XamT455UBS05ltR58ao3c-WVnA9Sw7c7vSGZX2aGp0HPm5GaC9VWttdL5O-TByBKOOWKYXK8nJFXuLYwjTSq9ysSpxFB5Ug2g45C2HVKnGvKtmW5L8vJrur1nTVGnv0KjQ/w640-h426/patient-routine-medical-check_23-2150493268.png)](https://www.healthsupplement24x7.com/get-pressure-aid) ### [_Order Pressure Aid From Its Official Website!_](https://www.healthsupplement24x7.com/get-pressure-aid) Understanding Pressure Aid Briefly -------------------------------------- [Pressure Aid](https://pressureaid.clubeo.com) is an all-natural supplement that works towards maintaining a healthy blood pressure level in the human body. Maintenance of blood pressure is crucial as an imbalance can create several problems in bodies’ other organs. It is designed to work almost immediately and within a few months of regular consumption will show you positive results in your body. [Pressure Aid](https://www.sympla.com.br/evento/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate/2133683) blood pressure support formula is free of toxins and hazardous materials and is made in an FDA-approved facility. The supplement doesn't comprise GMO or gluten and with good manufacturing practices. How Does Pressure Aid Work? ------------------------------- [Pressure Aid](https://pressureaid.clubeo.com/calendar/2023/08/24/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate) is primarily designed for people with high blood pressure who want to lower their blood pressure using natural ingredients. Here's how John Winters and the [Pressure Aid](https://pressureaidreviews.hashnode.dev/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate) formulation team describe the formula. Doctors may prescribe blood pressure medication to lower your blood pressure. However, popular blood pressure medication often comes with unwanted side effects.[Pressure Aid](https://www.ivoox.com/pressure-aid-peak-biome-for-regulate-healthy-blood-audios-mp3_rf_114813397_1.html) works differently, using a blend of natural ingredients to maintain healthy blood pressure levels. What Health Benefits Do You Get To Experience With Pressure Aid? --------------------------------------------------------------------- As you begin taking the [Pressure Aid](https://pdfhost.io/v/p0XVSC75C_Pressure_Aid_Peak_Biome_For_Regulate_Healthy_Blood_Pressure_And_Maintain_Heart_Rate) supplement, you will experience changes in your heart health, energy levels, blood circulation, and cholesterol as well. Let’s now know about them in detail as in how actually the intake of supplement bring changes in those areas: Pressure Aid Supports Your Heart Naturally [Pressure Aids](https://devfolio.co/projects/pressure-aid-peak-biome-5657)’s perfect mix of natural ingredients strengthens the heart muscles, which helps patients suffering from poor heart functions. After the external support from supplements, the heart will be healthy, and it is easier for the heart to pump blood throughout the blood, with less possibility of suffering a heart attack or stroke. Pressure Aid Boosts Your Energy Levels Pressure Aid boosts your metabolism. Its ingredients help in increasing the production of energy in the body. Thus, leaving you with an insurmountable amount of vitality and energy, which will help you live a healthy and active life. It might even promote weight loss through the achieved metabolism. Pressure Aid Helps In Smooth Blood Circulation [Pressure Aid](https://sketchfab.com/3d-models/pressure-aid-peak-biome-966bc69296db476b92faef42f20c6813) helps in increasing the oxygen in the blood concentration. It also helps in building a strong artery wall and prevents the clotting of blood, which in turn leads to smooth circulation of blood in the body. It is good for your blood vessels as well and keeps a close check on high blood pressure. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9-SMzAHVj1tHLkJ_ArA_ZzDZZWC_doYVtSriEvPkmQWAtqs_bAwWULQxK3rlwky_r9SWM-0YkKLUUPIpZp_P2-xAHUs2vARDUb2KwJhvkUsiTTKpehJc_Prc-0e904O6SVclXIdHeksPX7tXnqq6vKoztI_iLaYgEoh15lNV1pGMmPFTxH92K6oCtQyUe/w640-h608/Screenshot%20(3101).png)](https://www.healthsupplement24x7.com/get-pressure-aid) ### _[Get Pressure Aid today and start enjoying the benefits!](https://www.healthsupplement24x7.com/get-pressure-aid)_ What Ingredients Are Inside Pressure Aid? --------------------------------------------- There are eight main ingredients that make up the bulk of [Pressure Aid](https://colab.research.google.com/drive/1I2DEHCr8Awbg1TJzwfqJYn5O4xzO5_Td), including: Hibiscus (Roselle) - Hibiscus (Roselle) is a plant that grows on the high plateaus of the Andes Mountains, writes one source, where it was also suggested that its root has long been used in alternative medicine. In particular, among other unsupported claims, it is thought to treat male infertility. One study showed that Hibiscus (Roselle) could help regulate insulin resistance, thereby reducing or treating glucose and lipid metabolic diseases, which would have a positive impact on blood pressure levels. Black Garlic Extract - Black Garlic Extract is an Amazonian plant whose fruit can grow to the size of a coffee berry. Its high concentration of stimulants like caffeine, theophylline, and theobromine has earned it a spot in the energy drink market, while antioxidants like tannins, saponins, and catechins provide a slew of health benefits. Black Garlic Extract's capacity to encourage weight loss by boosting the metabolism may be of interest for Pressure Aid given that obesity has been related to a number of chronic ailments, including heart disease, type 2 diabetes, and even cancer. In terms of blood pressure levels, one source suggests that its caffeine source may alter how the body consumes sugar, perhaps worsening levels. Therefore, before moving forward, people should discuss this with their respective medical specialists. Vitamin K2 - Vitamin K2 is a solution made from the seeds of wine grapes. In a preliminary trial, type 2 diabetic high-risk cardiovascular participants were examined to see if GSE may enhance specific markers. The researchers showed that giving obese type 2 diabetic individuals 600mg of GSE daily for 4 weeks did indeed assist to reduce oxidative stress, glycaemia, and inflammation. They therefore concluded that GSE might play a therapeutic role in lowering cardiovascular risk as a result of this. Other reported benefits include reduced blood pressure, improved blood flow, bone strength, healthy brain function with age, and improved kidney function to name a few. Hawthorn Extract - The Hawthorn Extract is a fruit that has long been utilized medicinally by African tribes. Along with the fruit, the plant's leaves, roots, and bark have also been used to aid with digestion, boost satiety, lower cholesterol, and triglyceride levels, and perhaps improve weight loss (namely, reduction in waist circumference, and body fat percentage). It is important to note that these results stem from early research, therefore, more quality studies are needed to make inferences about a greater population. Olive Leaf Extract - Olive Leaf Extract is a plant that is well-known for its richness of antioxidants and their capacity to improve general health. An herb mostly found in traditional Chinese medicine, it is thought to have health benefits for both those with and without diabetes. This ingredient in particular has been demonstrated to accelerate the uptake of blood pressure in tissues, increase insulin synthesis, and enhance pancreatic cell function. The same site also cited a review of eight studies that discovered Olive Leaf Extract helped lower fasting blood pressure levels and enhance insulin sensitivity in persons with type 2 diabetes. CoQ10 - CoQ10 is a woody climbing shrub native to India, Africa, and Australia's tropical woodlands. Since ancient times, this shrub's leaves have been utilized in Ayurvedic medicine, specifically for diabetes, malaria, and snakebites. This chemical may stabilize blood pressure by lowering the desire for sugar. The latter, interestingly, is accomplished by making sugary meals less alluring. Furthermore, gymnema is believed to have anti-diabetic properties. This is because it has the capacity to inhibit intestinal receptors and so reduce the absorption of sugar. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVQ3R247l7DdH3wrOW_2xshoxidBx-H97UyIpI8pqn-I2aRLSxfgkV_vqhC51AvCT25r19n01vuEax_GeNJjrCV1Nkm1blaIMQsQznNF5U69wHdfBah-BmGqm4tlvXnoYgmk-PHFrB_2rDgg2AS5TOA5khfiS_5d-hAvDUqMgg5moRU5fUIz6O9F3IyscD/w640-h278/Screenshot%20(3099).png)](https://www.healthsupplement24x7.com/get-pressure-aid) ### [_Click Here To Buy Pressure Aid Get the Lowest Price From Official Website!_](https://www.healthsupplement24x7.com/get-pressure-aid) How To Take Pressure Aid Diabetes Supplement? -------------------------------------------------- It could not be less difficult to take an [Pressure Aid](https://colab.research.google.com/drive/1PH2Aak2Sbxv8FB7kX4ny-_chknaL9Mog) diabetes complement; all you want is 1 ml each day of this robust complement; it comes in a box containing 60 ml/2 FL OZ and, whilst taken continuously, lasts for 180 days This short daily ordinary simply just a few minutes, however over the years, it may have a big impact. Therefore, reflect onconsideration on taking the [Pressure Aid](https://colab.research.google.com/drive/18ggVawJEcJr_4Kbils8pybYg3t-Rgwwf) diabetes complement every day if you need to offer your widespread fitness and health a much-wanted raise. Is Pressure Aid Scam Or Legit? ----------------------------------- Yes it is legit not scam, It's possible that knowing that it's manufactured at a facility that's been authorized by the FDA and is GMP-certified right here in the United States would help you feel more at ease with the decision. Please make all purchases from the official website so that you can be absolutely confident that you are getting the genuine article and that the contents have not been altered in any way. Side Effects of Pressure Aid -------------------------------- One of the main disadvantages of using [Pressure Aid](https://colab.research.google.com/drive/13-hsKJkkK9mCZ7UAg1z4EBaK00WYwQIf) is the potential for side effects. 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One bottle – $49 (plus shipping) Three bottles – $39/per bottle (free shipping) Six bottles – $29/per bottle (free shipping) You must at least buy three bottles of this supplement to maintain healthy blood pressure levels and ensure improved overall health in the long run. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLZj6o4iC11hh_CtvpqfTyx_JRJmG24nuM3Bs1Wl2poIgYoA39__7AAuhPpgtwFx6AL_ED7bdgMHLpcifnFMWhz28cFzteNajeO348s2gP8wq-7Puli37_4kb43a9T11ow5QnHiLni8doiJWz29wBXOM4iKDc_eDDCnWfx5PzsynAWvQvBAiQE-eWxUIVy/w640-h468/Screenshot%20(3100).png)](https://www.healthsupplement24x7.com/get-pressure-aid) ### [_Must See: Due To High Demand, We Accept Limited Orders, Hurry Now_](https://www.healthsupplement24x7.com/get-pressure-aid) Do They Provide Money-Back Guarantee? ----------------------------------------- [Pressure Aid](https://pressureaid.clubeo.com/page/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate.html) offers a 180-days money-back guarantee in case of dissatisfaction by the customer in any form. You will get the entire purchase amount refunded back into your account if you are not satisfied with the product or its results. Make sure to contact the support team of Pressure Aid within 180 days from the date of purchase. The refund amount will be processed within 2 days after receiving your return. You are eligible to get a refund even if you return the empty bottles. Final Verdict ------------------ The evidence presented shows that [Pressure Aid](https://www.scoop.it/topic/pressure-aid-by-peak-biome-pressure-aid) is an intelligent, natural product designed to reduce blood pressure levels. Many of the ingredients that have been chosen in this formula are known to reduce insulin resistance and improve overall immunity, and energy metabolism, as well as address various conditions associated with blood pressure variations such as oxidative stress and inflammation. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgYn4uELHyUxFacpreI2TEMIu2CoJ8kpBEEdS77Du0HNbc74Loz-lbMkimP7WiL4BJZdGkO88NxEP_h5gI3329EGZGZY7lZx06mfwQTQafS7ISnTgqduDE-z8eIBPhDqr4egUPRj1UgB0WyUn76hnmUm1k5CfShmSakraedu7S-rJmLiaxpLsDfePXROzPu/w640-h420/IMG_5164.png)](https://www.healthsupplement24x7.com/get-pressure-aid) ### _[Order your supply of Pressure Aid now and start enjoying the benefits!](https://www.healthsupplement24x7.com/get-pressure-aid)_ [https://healthsupplements24x7.blogspot.com/2023/08/pressure-aid.html](https://healthsupplements24x7.blogspot.com/2023/08/pressure-aid.html) [https://peak-biome-pressure-aid-1.jimdosite.com/](https://peak-biome-pressure-aid-1.jimdosite.com/) [https://www.sympla.com.br/evento/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate/2133683](https://www.sympla.com.br/evento/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate/2133683) [https://pressureaid.clubeo.com](https://pressureaid.clubeo.com) [https://pressureaid.clubeo.com/calendar/2023/08/24/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate](https://pressureaid.clubeo.com/calendar/2023/08/24/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate) [https://pressureaid.clubeo.com/page/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate.html](https://pressureaid.clubeo.com/page/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate.html) [https://pressureaid.clubeo.com/page/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate-1.html](https://pressureaid.clubeo.com/page/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate-1.html) [https://www.scoop.it/topic/pressure-aid-by-peak-biome-pressure-aid](https://www.scoop.it/topic/pressure-aid-by-peak-biome-pressure-aid) [https://pressureaidreviews.hashnode.dev/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate](https://pressureaidreviews.hashnode.dev/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate) [https://soundcloud.com/peak-biome-pressure-aid/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate](https://soundcloud.com/peak-biome-pressure-aid/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate) [https://www.ivoox.com/pressure-aid-peak-biome-for-regulate-healthy-blood-audios-mp3\_rf\_114813397\_1.html](https://www.ivoox.com/pressure-aid-peak-biome-for-regulate-healthy-blood-audios-mp3_rf_114813397_1.html) [https://pdfhost.io/v/p0XVSC75C\_Pressure\_Aid\_Peak\_Biome\_For\_Regulate\_Healthy\_Blood\_Pressure\_And\_Maintain\_Heart\_Rate](https://pdfhost.io/v/p0XVSC75C_Pressure_Aid_Peak_Biome_For_Regulate_Healthy_Blood_Pressure_And_Maintain_Heart_Rate) [https://colab.research.google.com/drive/13-hsKJkkK9mCZ7UAg1z4EBaK00WYwQIf](https://colab.research.google.com/drive/13-hsKJkkK9mCZ7UAg1z4EBaK00WYwQIf) [https://colab.research.google.com/drive/1cMdbBst3ya3v5iAukdIkoihclQ6xb5Ll](https://colab.research.google.com/drive/1cMdbBst3ya3v5iAukdIkoihclQ6xb5Ll) [https://colab.research.google.com/drive/1PH2Aak2Sbxv8FB7kX4ny-\_chknaL9Mog](https://colab.research.google.com/drive/1PH2Aak2Sbxv8FB7kX4ny-_chknaL9Mog) [https://colab.research.google.com/drive/18ggVawJEcJr\_4Kbils8pybYg3t-Rgwwf](https://colab.research.google.com/drive/18ggVawJEcJr_4Kbils8pybYg3t-Rgwwf) [https://colab.research.google.com/drive/1I2DEHCr8Awbg1TJzwfqJYn5O4xzO5\_Td](https://colab.research.google.com/drive/1I2DEHCr8Awbg1TJzwfqJYn5O4xzO5_Td) [https://events.humanitix.com/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate](https://events.humanitix.com/pressure-aid-peak-biome-for-regulate-healthy-blood-pressure-and-maintain-heart-rate) [https://sleucks-physiurg-wraungly.yolasite.com/](https://sleucks-physiurg-wraungly.yolasite.com/) [https://form.jotform.com/pressureaid/peak-biome-pressure-aid](https://form.jotform.com/pressureaid/peak-biome-pressure-aid) [https://devfolio.co/projects/pressure-aid-peak-biome-5657](https://devfolio.co/projects/pressure-aid-peak-biome-5657) [https://sketchfab.com/3d-models/pressure-aid-peak-biome-966bc69296db476b92faef42f20c6813](https://sketchfab.com/3d-models/pressure-aid-peak-biome-966bc69296db476b92faef42f20c6813)
zxvix/pubmed_nonacademic	2023-08-25T04:12:14.000Z	[ "region:us" ]	zxvix	null	null	null	0	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: MedlineCitation struct: - name: PMID dtype: int32 - name: DateCompleted struct: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: NumberOfReferences dtype: int32 - name: DateRevised struct: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: Article struct: - name: Abstract struct: - name: AbstractText dtype: string - name: ArticleTitle dtype: string - name: AuthorList struct: - name: Author sequence: - name: LastName dtype: string - name: ForeName dtype: string - name: Initials dtype: string - name: CollectiveName dtype: string - name: Language dtype: string - name: GrantList struct: - name: Grant sequence: - name: GrantID dtype: string - name: Agency dtype: string - name: Country dtype: string - name: PublicationTypeList struct: - name: PublicationType sequence: string - name: MedlineJournalInfo struct: - name: Country dtype: string - name: ChemicalList struct: - name: Chemical sequence: - name: RegistryNumber dtype: string - name: NameOfSubstance dtype: string - name: CitationSubset dtype: string - name: MeshHeadingList struct: - name: MeshHeading sequence: - name: DescriptorName dtype: string - name: QualifierName dtype: string - name: PubmedData struct: - name: ArticleIdList sequence: - name: ArticleId sequence: string - name: PublicationStatus dtype: string - name: History struct: - name: PubMedPubDate sequence: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: ReferenceList sequence: - name: Citation dtype: string - name: CitationId dtype: int32 - name: text dtype: string - name: title dtype: string - name: original_text dtype: string splits: - name: test num_bytes: 3861659.43 num_examples: 991 download_size: 2129097 dataset_size: 3861659.43 --- # Dataset Card for "pubmed_nonacademic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RahmaSadder/test3	2023-08-24T10:05:46.000Z	[ "license:apache-2.0", "region:us" ]	RahmaSadder	null	null	null	0	--- license: apache-2.0 ---
lakshya1999/nerlg	2023-08-24T09:31:41.000Z	[ "license:mit", "region:us" ]	lakshya1999	null	null	null	0	--- license: mit ---
GarlicBread99/StormtrooperForceUnleashed	2023-08-24T09:44:09.000Z	[ "region:us" ]	GarlicBread99	null	null	null	0	Entry not found
Alignment-Lab-AI/Open-Orca-Directories	2023-08-24T09:47:21.000Z	[ "region:us" ]	Alignment-Lab-AI	null	null	null	0	Entry not found
Livewellcbdgummies/Livewellcbdgumies	2023-08-24T10:04:11.000Z	[ "region:us" ]	Livewellcbdgummies	null	null	null	0	[Live Well CBD Gummies](https://www.facebook.com/groups/664811861991540) which have the best formula for memory retention and reducing health concerns, should be used to enhance healthy brain cells and mental functioning. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjFXt6FiLilwZh7CifCB-GvozM46z4artw8EQ1EmBCbOTN9wdrlatyxAdl18W0iGp0azaf7e3qZNBajxfj4eM1HiDt1U-I1VTknd-4WLgmYZ8MOmVt8Z199ZPqidBxVhEYZjcMvJHaYcM6YM3btHul7AwSXEhGqjrIDD5OoguMAo0thTUeq0sTzsi8KwA8/w640-h640/1635481825935.jpg)](https://www.glitco.com/get-live-well-cbd-gummies) The Live Well CBD Gummies are reviewed in this article to aid in treating sexual dysfunction. These gummies are a tasty and practical way to revive your sexual prowess. The Live Well CBD Gummies are free of any additives and are manufactured with top-shelf pure CBD extract, natural ingredients, and flavors. 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TinyPixel/l2-oasst	2023-09-02T17:14:19.000Z	[ "region:us" ]	TinyPixel	null	null	null	0	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9309644 num_examples: 8274 download_size: 5133098 dataset_size: 9309644 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "l2-oasst" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
madhan2301/java-merge-dataset	2023-08-24T10:03:59.000Z	[ "region:us" ]	madhan2301	null	null	null	0	Entry not found
fastian1/flair_axial_patches	2023-08-24T14:01:21.000Z	[ "region:us" ]	fastian1	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 4703325663.87 num_examples: 35681 - name: validation num_bytes: 1182787026.758 num_examples: 8973 download_size: 1555687513 dataset_size: 5886112690.628 --- # Dataset Card for "flair_axial_patches" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/fw_baseline_train_1000_eval_100	2023-08-24T10:15:17.000Z	[ "region:us" ]	tyzhu	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: eval_find_word path: data/eval_find_word-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: text dtype: string splits: - name: train num_bytes: 172392 num_examples: 1000 - name: eval_find_word num_bytes: 17146 num_examples: 100 - name: validation num_bytes: 17146 num_examples: 100 download_size: 11381 dataset_size: 206684 --- # Dataset Card for "fw_baseline_train_1000_eval_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/fw_baseline_train_10000_eval_100	2023-08-24T10:15:40.000Z	[ "region:us" ]	tyzhu	null	null	null	0	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: eval_find_word path: data/eval_find_word-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: text dtype: string splits: - name: train num_bytes: 1724070 num_examples: 10000 - name: eval_find_word num_bytes: 17146 num_examples: 100 - name: validation num_bytes: 17146 num_examples: 100 download_size: 849667 dataset_size: 1758362 --- # Dataset Card for "fw_baseline_train_10000_eval_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
madhan2301/merge-code-dataset	2023-08-24T10:19:07.000Z	[ "region:us" ]	madhan2301	null	null	null	0	Entry not found
waliiid/data	2023-08-24T10:19:39.000Z	[ "region:us" ]	waliiid	null	null	null	0	Entry not found
RahmaSadder/test4	2023-08-24T10:29:28.000Z	[ "license:apache-2.0", "region:us" ]	RahmaSadder	null	null	null	0	--- license: apache-2.0 ---

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